students' learning skills research

Featured Topics

Featured series.

A series of random questions answered by Harvard experts.

Explore the Gazette

Read the latest.

Science is making anti-aging progress. But do we want to live forever?

Epic science inside a cubic millimeter of brain

What is ‘original scholarship’ in the age of AI?

Lessons in learning.

Sean Finamore ’22 (left) and Xaviera Zime ’22 study during a lecture in the Science Center.

Photos by Kris Snibbe/Harvard Staff Photographer

Peter Reuell

Harvard Staff Writer

Study shows students in ‘active learning’ classrooms learn more than they think

For decades, there has been evidence that classroom techniques designed to get students to participate in the learning process produces better educational outcomes at virtually all levels.

And a new Harvard study suggests it may be important to let students know it.

The study , published Sept. 4 in the Proceedings of the National Academy of Sciences, shows that, though students felt as if they learned more through traditional lectures, they actually learned more when taking part in classrooms that employed so-called active-learning strategies.

Lead author Louis Deslauriers , the director of science teaching and learning and senior physics preceptor, knew that students would learn more from active learning. He published a key study in Science in 2011 that showed just that. But many students and faculty remained hesitant to switch to it.

“Often, students seemed genuinely to prefer smooth-as-silk traditional lectures,” Deslauriers said. “We wanted to take them at their word. Perhaps they actually felt like they learned more from lectures than they did from active learning.”

In addition to Deslauriers, the study is authored by director of sciences education and physics lecturer Logan McCarty , senior preceptor in applied physics Kelly Miller, preceptor in physics Greg Kestin , and Kristina Callaghan, now a physics lecturer at the University of California, Merced.

The question of whether students’ perceptions of their learning matches with how well they’re actually learning is particularly important, Deslauriers said, because while students eventually see the value of active learning, initially it can feel frustrating.

“Deep learning is hard work. The effort involved in active learning can be misinterpreted as a sign of poor learning,” he said. “On the other hand, a superstar lecturer can explain things in such a way as to make students feel like they are learning more than they actually are.”

To understand that dichotomy, Deslauriers and his co-authors designed an experiment that would expose students in an introductory physics class to both traditional lectures and active learning.

For the first 11 weeks of the 15-week class, students were taught using standard methods by an experienced instructor. In the 12th week, half the class was randomly assigned to a classroom that used active learning, while the other half attended highly polished lectures. In a subsequent class, the two groups were reversed. Notably, both groups used identical class content and only active engagement with the material was toggled on and off.

Following each class, students were surveyed on how much they agreed or disagreed with statements such as “I feel like I learned a lot from this lecture” and “I wish all my physics courses were taught this way.” Students were also tested on how much they learned in the class with 12 multiple-choice questions.

When the results were tallied, the authors found that students felt as if they learned more from the lectures, but in fact scored higher on tests following the active learning sessions. “Actual learning and feeling of learning were strongly anticorrelated,” Deslauriers said, “as shown through the robust statistical analysis by co-author Kelly Miller, who is an expert in educational statistics and active learning.”

Those results, the study authors are quick to point out, shouldn’t be interpreted as suggesting students dislike active learning. In fact, many studies have shown students quickly warm to the idea, once they begin to see the results. “In all the courses at Harvard that we’ve transformed to active learning,” Deslauriers said, “the overall course evaluations went up.”

Co-author Kestin, who in addition to being a physicist is a video producer with PBS’ NOVA, said, “It can be tempting to engage the class simply by folding lectures into a compelling ‘story,’ especially when that’s what students seem to like. I show my students the data from this study on the first day of class to help them appreciate the importance of their own involvement in active learning.”

McCarty, who oversees curricular efforts across the sciences, hopes this study will encourage more of his colleagues to embrace active learning.

“We want to make sure that other instructors are thinking hard about the way they’re teaching,” he said. “In our classes, we start each topic by asking students to gather in small groups to solve some problems. While they work, we walk around the room to observe them and answer questions. Then we come together and give a short lecture targeted specifically at the misconceptions and struggles we saw during the problem-solving activity. So far we’ve transformed over a dozen classes to use this kind of active-learning approach. It’s extremely efficient — we can cover just as much material as we would using lectures.”

A pioneer in work on active learning, Balkanski Professor of Physics and Applied Physics Eric Mazur hailed the study as debunking long-held beliefs about how students learn.

“This work unambiguously debunks the illusion of learning from lectures,” he said. “It also explains why instructors and students cling to the belief that listening to lectures constitutes learning. I recommend every lecturer reads this article.”

Dean of Science Christopher Stubbs , Samuel C. Moncher Professor of Physics and of Astronomy, was an early convert. “When I first switched to teaching using active learning, some students resisted that change. This research confirms that faculty should persist and encourage active learning. Active engagement in every classroom, led by our incredible science faculty, should be the hallmark of residential undergraduate education at Harvard.”

Ultimately, Deslauriers said, the study shows that it’s important to ensure that neither instructors nor students are fooled into thinking that lectures are the best learning option. “Students might give fabulous evaluations to an amazing lecturer based on this feeling of learning, even though their actual learning isn’t optimal,” he said. “This could help to explain why study after study shows that student evaluations seem to be completely uncorrelated with actual learning.”

This research was supported with funding from the Harvard FAS Division of Science.

Share this article

You might like.

Nobel laureate details new book, which surveys research, touches on larger philosophical questions

Researchers publish largest-ever dataset of neural connections

Symposium considers how technology is changing academia

Excited about new diet drug? This procedure seems better choice.

Study finds minimally invasive treatment more cost-effective over time, brings greater weight loss

How far has COVID set back students?

An economist, a policy expert, and a teacher explain why learning losses are worse than many parents realize

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Fostering students’ motivation towards learning research skills: the role of autonomy, competence and relatedness support

Louise maddens.

1 Centre for Instructional Psychology and Technology, Faculty of Psychology and Educational Sciences, KU Leuven and KU Leuven Campus Kulak Kortrijk, Etienne Sabbelaan 51 – bus 7800, 8500 Kortrijk, Belgium

2 Itec, imec Research Group at KU Leuven, imec, Leuven, Belgium

3 Vives University of Applied Sciences, Kortrijk, Belgium

Fien Depaepe

Annelies raes.

In order to design learning environments that foster students’ research skills, one can draw on instructional design models for complex learning, such as the 4C/ID model (in: van Merriënboer and Kirschner, Ten steps to complex learning, Routledge, London, 2018). However, few attempts have been undertaken to foster students’ motivation towards learning complex skills in environments based on the 4C/ID model. This study explores the effects of providing autonomy, competence and relatedness support (in Deci and Ryan, Psychol Inquiry 11(4): 227–268, https://doi.org/10.1207/S15327965PLI1104_01, 2000) in a 4C/ID based online learning environment on upper secondary school behavioral sciences students’ cognitive and motivational outcomes. Students’ cognitive outcomes are measured by means of a research skills test consisting of short multiple choice and short answer items (in order to assess research skills in a broad way), and a research skills task in which students are asked to integrate their skills in writing a research proposal (in order to assess research skills in an integrative manner). Students’ motivational outcomes are measured by means of students’ autonomous and controlled motivation, and students’ amotivation. A pretest-intervention-posttest design was set up in order to compare 233 upper secondary school behavioral sciences students’ outcomes among (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. Both learning environments proved equally effective in improving students’ scores on the research skills test. Students in the need supportive condition scored higher on the research skills task compared to their peers in the baseline condition. Students’ autonomous and controlled motivation were not affected by the intervention. Although, unexpectedly, students’ amotivation increased in both conditions, students’ amotivation was lower in the need supportive condition compared to students in the baseline condition. Theoretical relationships were established between students’ need satisfaction, students’ motivation (autonomous, controlled, and amotivation), and students’ cognitive outcomes. These findings are discussed taking into account the COVID-19 affected setting in which the study took place.

Introduction

Several scholars have argued that the process of learning research skills is often obstructed by motivational problems (Lehti & Lehtinen, 2005 ; Murtonen, 2005 ). Some even describe these issues as students having an aversion towards research (Pietersen, 2002 ). Examples of motivational problems are that students experience research courses as boring, inaccessible, or irrelevant to their daily lives (Braguglia & Jackson, 2012 ). In a research synthesis on teaching and learning research methods, Earley ( 2014 ) argues that students fail to see the relevance of research methods courses, are anxious or nervous about the course, are uninterested and unmotivated to learn the material, and have poor attitudes towards learning research skills. It should be mentioned that the studies mentioned above focused on the field of higher university education. In upper secondary education, to date, students’ motivation towards learning research skills has rarely been studied. As difficulties while learning research seem to relate to problems involving students’ previous experiences regarding learning research skills (Murtonen, 2005 ), we argue that fostering students’ motivation from secondary education onwards is a promising area of research.

The current study combines insights from instructional design theory and self-determination theory (SDT, Deci & Ryan, 2000 ), in order to investigate the cognitive and motivational effects of providing psychological need support (support for the need for autonomy, competence and relatedness) in a 4C/ID based (van Merriënboer & Kirschner, 2018 ) online learning environment fostering upper secondary schools students’ research skills. In the following section, we elaborate on the definition of research skills in the understudied domain of behavioral sciences; on 4C/ID (van Merriënboer & Kirschner, 2018 ) as an instructional design model for complex learning; and on self-determination theory and its related need theory (Deci & Ryan, 2000 ). In addition, the research questions addressed in the current study are outlined.

Conceptual framework

Research skills.

As described by Fischer et al., ( 2014 , p. 29), we define research skills 1 as a broad set of skills used “to understand how scientific knowledge is generated in different scientific disciplines, to evaluate the validity of science-related claims, to assess the relevance of new scientific concepts, methods, and findings, and to generate new knowledge using these concepts and methods”. Furthermore, eight scientific activities learners engage in while performing research are distinguished, namely: (1) problem identification, (2) questioning, (3) hypothesis generation, (4) construction and redesign of artefacts, (5) evidence generation, (6) evidence evaluation, (7) drawing conclusions, and (8) communicating and scrutinizing (Fischer et al., 2014 ). Fischer et al. ( 2014 ) argue that both the nature of, and the weights attributed to each of these activities, differ between domains. Intervention studies aiming to foster research skills are almost exclusively situated in natural sciences domains (Engelmann et al., 2016 ), leaving behavioral sciences domains largely understudied. The current study focuses on research skills in the understudied domain of behavioral sciences. We refer to the domain of behavioral sciences as the study of questions related to how people behave, and why they do so. Human behavior is understood in its broadest sense, and is the study of object in fields of psychology, educational sciences, cultural and social sciences.

The design of the learning environments used in this study is based on an existing instructional design model, namely the 4C/ID model (van Merriënboer & Kirschner, 2018 ). The 4C/ID model has been proven repeatedly effective in fostering complex skills (Costa et al., 2021 ), and thus drew our attention for the case of research skills, as research skills can be considered complex skills (it requires learners to integrate knowledge, skills and attitudes while performing complex learning tasks). Since the 4C/ID model focusses on supporting students’ cognitive outcomes, it might not be considered as relevant from a motivational point of view. However, since we argue that a deliberately designed learning environment from a cognitive point of view is an important prerequisite to provide qualitative motivational support, we briefly sketch the 4C/ID model and its characteristics. The 4C/ID model has a comprehensive character, integrating insights from different theories and models (Merrill, 2002 ), and highlights the relevance of four crucial components: learning tasks, supportive information, part task-practice, and just-in-time information. Central characteristics of these four components are that (a) high variability in authentic learning tasks is needed in order to deal with the complexity of the task; (b) supportive information is provided to the students in order to help them build mental models and strategies for solving the task under study (Cook & McDonald, 2008 ); (c) part-task practice is provided for recurrent skills that need to be automated; and (d) just-in-time (procedural) information is provided for recurrent skills.

Taking into account students’ cognitive struggles regarding research skills, and the existing research on the role of support in fostering research skills (see for example de Jong & van Joolingen, 1998 ), the 4C/ID model was found suitable to design a learning environment for research skills. This is partly because of its inclusion of (almost) all of the support found effective in the literature on research skills, such as providing direct access to domain information at the appropriate moment, providing learners with assignments, including model progression, the importance of students’ involvement in authentic activities, and so on (Chi, 2009 ; de Jong, 2006 ; de Jong & van Joolingen, 1998 ; Engelmann et al., 2016 ). While mainly implemented in vocational oriented programs, the 4C/ID model has been proposed as a good model to design learning environments aiming to foster research skills as well (Bastiaens et al., 2017 ; Maddens et al., 2020b ). Indeed, acquiring research skills requires complex learning processes (such as coordinating different constituent skills). Overall, the 4C/ID model can be considered to be highly suitable for designing learning environments aiming to foster research skills. Given its holistic design approach, it helps “to deal with complexity without losing sight of the interrelationships between the elements taught” (van Merriënboer & Kirschner, 2018 , p. 5).

Although the 4C/ID model has been used widely to construct learning environments enhancing students’ cognitive outcomes (see for example Fischer, 2018 ), research focusing on students’ motivational outcomes related to the 4C/ID model is scarce (van Merriënboer & Kirschner, 2018 ). Van Merriënboer and Kirschner ( 2018 ) suggest self-determination theory (SDT; Deci & Ryan, 2000 ) and its related need theory as a sound theoretical framework to investigate motivation in relation to 4C/ID.

Self-determination theory

Self-determination theory (SDT; Deci & Ryan, 2000 ) provides a broad framework for the study of motivation and distinguishes three types of motivation: amotivation (a lacking ability to self-regulate with respect to a behaviour), extrinsic motivation (extrinsically motivated behaviours, be they self-determined versus controlled), and intrinsic motivation (the ‘highest form’ of self-determined behaviour) (Deci & Ryan, 2000 ). According to Deci and Ryan ( 2000 , p. 237), intrinsic motivation can be considered “a standard against which the qualities of an extrinsically motivated behavior can be compared to determine its degree of self-determination”. Moreover, the authors (Deci & Ryan, 2000 , p. 237) argue that “extrinsic motivation does not typically become intrinsic motivation”. As the current study focuses on research skills in an academic context in which students did not voluntary chose to learn research skills, and thus learning research skills can be considered instrumental (directed to attaining a goal), the current study focuses on students’ amotivation, and students’ extrinsic motivation, realistically striving for the most self-determined types of extrinsic motivation.

Four types of extrinsic motivation are distinguished by SDT (external regulation, introjection, identification, and integration). These types can be categorized in two overarching types of motivation (autonomous and controlled motivation). Autonomous motivation contains the integrated and identified regulation towards a task (be it because the task is considered interesting, or because the task is considered personally relevant respectively). Controlled motivation refers to the external and introjected regulation towards the task (as a consequence of external or internal pressure respectively) (Vansteenkiste et al., 2009 ). More autonomous types of motivation have been found to be related to more positive cognitive and motivational outcomes (Deci & Ryan, 2000 ).

SDT further maintains that one should consider three innate psychological needs related to students’ motivation. These needs are the need for autonomy, the need for competence, and the need for relatedness. The need for autonomy can be described as the need to experience activities as being “concordant with one’s integrated sense of self” (Deci & Ryan, 2000 , p. 231). The need for competence refers to the need to feel effective when dealing with the environment (Deci & Ryan, 2000 ). The need for relatedness contains the need to have close relationships with others, including peers and teachers (Deci & Ryan, 2000 ). The satisfaction of these needs is hypothesized to be related to more internalization, and thus to more autonomous types of motivation (Deci & Ryan, 2000 ). This relationship has been studied frequently (for a recent overview, see Vansteenkiste et al., 2020 ). Indeed, research established the positive relationships between perceived autonomy (see for example Deci et al., 1996 ), perceived competence (see for example Vallerand & Reid, 1984 ), and perceived relatedness (see for example Ryan & Grolnick, 1986 for a self-report based study) with students’ more positive motivational outcomes. Apart from students’ need satisfaction, several scholars also aim to investigate need frustration as a different notion, as “it involves an active threat of the psychological needs (rather than a mere absence of need satisfaction)” (Vansteenkiste et al., 2020 , p. 9). In what follows, possible operationalizations are defined for the three needs.

Possible operationalizations of autonomy need support found in the literature are: teachers accepting irritation or negative feelings related to aspects of a task perceived as “uninteresting” (Reeve, 2006 ; Reeve & Jang, 2006 ; Reeve et al., 2002 ); providing a meaningful rationale in order to explain the value/usefulness of a certain task and stressing why involving in the task is important or why a rule exists (Deci & Ryan, 2000 ); using autonomy-supportive, inviting language (Deci et al., 1996 ); and allowing learners to regulate their own learning and to work at their own pace (Martin et al., 2018 ). Related to competence support, possible operationalizations are: providing a clear task rationale and providing structure (Reeve, 2006 ; Vansteenkiste et al., 2012 ); providing informational positive feedback after a learning activity (Deci et al., 1996 ; Martin et al., 2018 ; Vansteenkiste et al., 2012 ); providing an indication of progress and dividing content into manageable blocks (Martin et al., 2018 ; Schunk, 2003 ); and evaluating performance by means of previously introduced criteria (Ringeisen & Bürgermeister, 2015 ). Possible operationalizations concerning relatedness support are: teacher’s relational supports (Ringeisen & Bürgermeister, 2015 ); encouraging interaction between course participants and providing opportunities for learners to connect with each other (Butz & Stupnisky, 2017 ; van Merriënboer & Kirschner, 2018 ); using a warm and friendly approach or welcoming learners personally into a course (Martin et al., 2018 ); and offering a platform for learners to share ideas and to connect (Butz & Stupnisky, 2017 ; Martin et al., 2018 ).

In the current research, SDT is selected as a theoretical framework to investigate students’ motivation towards learning research skills, as, in contrast to other more purely goal-directed theories, it includes the concept of innate psychological needs or the Basic Psychological Need Theory (Deci & Ryan, 2000 ; Ryan, 1995 ; Vansteenkiste et al., 2020 ), and it describes the relation between these perceived needs and students’ autonomous motivation: higher levels of perceived needs relate to more autonomous forms of motivation. The inclusion of this need theory is considered an advantage in the case of research skills because research revealed problems of students with respect to both their feelings of competence in relation to research skills (Murtonen, 2005 ), as their feelings of autonomy in relation to research skills (Martin et al., 2018 ), as was indicated in the introduction. As such, fostering students’ psychological needs while learning research skills seems a promising way of fostering students’ motivation towards learning research skills.

4C/ID and SDT

One study (Bastiaens et al., 2017 ) was found to implement need support in 4C/ID based learning environments, comparing a traditional module, a 4C/ID based module and an autonomy supportive 4C/ID based module in a vocational undergraduate education context. Autonomy support was operationalized by means of providing choice to the learners. No main effect of the conditions was found on students’ motivation. Surprisingly, providing autonomy support did also not lead to an increase in students’ autonomy satisfaction. Similarly, no effects were found on students’ relatedness and competence satisfaction. Remarkably, students did qualitatively report positive experiences towards the need support, but this did not reflect in their quantitatively reported need experiences. In a previous study performed in the current research trajectory, Maddens et al. ( under review ) investigated the motivational effects of providing autonomy support in a 4C/ID based online learning environment fostering students’ research skills, compared to a learning environment not providing such support. Autonomy support was operationalized as stressing task meaningfulness to the students. Based on insights from self-determination theory, it was hypothesized that students in the autonomy condition would show more positive motivational outcomes compared to students in the baseline condition. However, results showed that students’ motivational outcomes appeared to be unaffected by the autonomy support. One possible explanation for this unexpected finding was that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support (Deci & Ryan, 2000 ; Niemiec & Ryan, 2009 ), and thus, that the intervention was insufficiently powerful for effects to occur. Autonomy support has often been manipulated in experimental research (Deci et al., 1994 ; Reeve et al., 2002 ; Sheldon & Filak, 2008 ). However, the three needs are rarely simultaneously manipulated (Sheldon & Filak, 2008 ).

Integrated need support

Although not making use of 4C/ID based learning environments, some scholars have focused on the impact of integrated (autonomy, competence and relatedness) need support on learners’ motivation. For example, Raes and Schellens ( 2015 ) found differential effects of a need supportive inquiry environment on upper secondary school students’ motivation: positive effects on autonomous motivation were only found in students in a general track, and not in students in a science track. This indicates that motivational effects of need-supportive environments might differ between tracks and disciplines. However, Raes and Schellens ( 2015 ) did not experimentally manipulate need support, as the learning environment was assumed to be need-supportive and was not compared to a non-need supportive learning environment. Pioneers in manipulating competence, relatedness and autonomy support in one study are Sheldon and Filak ( 2008 ), predicting need satisfaction and motivation based on a game-learning experience with introductory psychology students. Relatedness support (mainly operationalized by emphasizing interest in participants’ experiences in a caring way) had a significant effect on intrinsic motivation. Competence support (mainly operationalized by means of explicating positive expectations) had a marginal significant effect on intrinsic motivation. No main effects on intrinsic motivation were found regarding autonomy support (mainly operationalized by means of emphasizing choice, self-direction and participants’ perspective upon the task). However, as is often the case in motivational research based on SDT, the task at hand was quite straight forward (a timed task in which students try to form as many words as possible from a 4 × 4 letter grid), and thus, the applicability of the findings for providing need support in 4C/ID based learning environments for complex learning might be limited.

In the preceding section, several operationalizations of need support were discussed. Deci and Ryan ( 2000 ) argue that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support. However, such integrated need support has rarely been empirically studied (Sheldon & Filak, 2008 ). In addition, research investigating how need support can be implemented in learning environments based on the 4C/ID model is particularly scarce (van Merriënboer & Kirschner, 2018 ). This study aims to combine insights from instructional design theory for complex learning (van Merriënboer & Kirschner, 2018 ) and self-determination theory (Deci & Ryan, 2000 ) in order to investigate the motivational effects of providing need support in a 4C/ID based learning environment for students’ research skills. A pretest-intervention-posttest design is set up in order to compare 233 upper secondary school behavioral sciences students’ cognitive and motivational outcomes among two conditions: (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. The following research questions are answered based on a combination of quantitative and qualitative data (see ‘method’): (1) Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task? ; ( 2) What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes (i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)? ; (3) What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)? ; (4) How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment? .

The first three questions are answered by means of quantitative data. Since the learning environment is constructed in line with existing instructional design principles for complex learning, we hypothesize that both learning environments will succeed in improving students’ research skills (RQ1). Relying on insights from self-determination theory (Deci & Ryan, 2000 ), we hypothesize that providing need support will enhance students’ autonomous motivation (RQ2). In addition, we hypothesize students’ need satisfaction to be positively related to students’ autonomous motivation (RQ3). These hypotheses on the relationship between students’ needs and students’ motivation rely on Vallerands’ ( 1997 ) finding that changes in motivation can be largely explained by students’ perceived competence, autonomy and relatedness (as psychological mediators). More specifically, Vallerand ( 1997 ) argues that environmental factors (in this case the characteristics of a learning environment) influence students’ perceptions of competence, autonomy, and relatedness, which, in turn, influence students’ motivation and other affective outcomes. In addition, based on the self-determination literature (Deci & Ryan, 2000 ), we expect students’ motivation to be positively related to students’ cognitive outcomes. In order to answer the fourth research question, qualitative data (students’ qualitative feedback on the learning environments) is analysed and categorized based on the need satisfaction and need frustration concepts (RQ4) in order to thoroughly capture the meaning of the quantitative results collected in light of RQ1–3. No hypotheses are formulated in this respect.

Methodology

Participants.

The study took place in authentic classroom settings in upper secondary behavioral sciences classes. In total, 233 students from 12 classes from eight schools in Flanders participated in the study. All participants are 11th or 12th grade students in a behavioral sciences track 2 in general upper secondary education in Flanders (Belgium). Classes were randomly assigned to one out of two experimental conditions. Of all 233 students, 105 students (with a mean age of 16.32, SD 0.90) worked in the baseline condition (of which 62% 11th grade students, 36% 12th grade students, and 2% not determined; and of which 31% male, 68% female, and 1% ‘other’), and 128 students (with a mean age of 16.02, SD 0.59) worked in the need supportive condition (of which 80% 11th grade students, and 20% 12th grade students; and of which 19% male, and 81% female). As the current study did not randomly assign students within classes to one out of the two conditions, this study should be considered quasi-experimental. Full randomization was considered but was not feasible as students worked in the learning environments in class, and would potentially notice the experimental differences when observing their peers working in the learning environment. As such, we argued that this would potentially cause bias in the study. By taking into account students’ pretest scores on the relevant variables (cognitive and motivational outcomes) as covariates, we aimed to adjust for inter-conditional differences. No such differences were found for students’ autonomous motivation t (226) =  − 0.115, p  < 0.909, d  = 0.015, and students’ amotivation t (226) =  − 0.658, p  < 0.511, d  =  − 0.088. However, differences were observed for students’ controlled motivation t (226) =  − 2.385, p  < 0.018, d  =  − 0.318, and students’ scores on the LRST pretest t (225) = − 5.200, p  < 0.001, d  =  − 0.695.

Study design and procedure

In a pretest session of maximum two lesson hours, the Leuven Research Skills Test (LRST, Maddens et al., 2020a ), the Academic Self-Regulation Scale (ASRS, Vansteenkiste et al., 2009 ), and four items related to students’ amotivation (Aydin et al., 2014 ) were administered in class via an online questionnaire, under supervision of the teacher. In the subsequent eight weeks, participants worked in the online learning environment, one hour a week. Out of the 233 participating students, 105 students studied in a baseline online learning environment. The baseline online learning environment 3 is systematically designed using existing instructional design principles for complex learning based on the 4C/ID model (van Merriënboer & Kirschner, 2018 ). All four components of the 4C/ID model were taken into account in the design process: regarding the first component, the learning tasks included real-life, authentic cases. More specifically, tasks were selected from the domains of psychology, educational sciences and sociology. As such, there was a large variety in the cases used in the learning tasks. This large variety in learning tasks is expected to facilitate transfer of learners’ research skills in a wide range of contexts. Furthermore, the tasks were ill-structured and required learners to make judgments, in order to provoke deep learning processes. Regarding the second component, supportive information was provided for complex tasks in the learning environment, such as formulating a research question, where students can consult general information on what constitutes a good research question, can consult examples or demonstrations of this general information, and can receive cognitive feedback on their answers (for example by means of example answers). Examples of the implementation of the third component (procedural information) are the provision of information on how to recognize a dependent and an independent variable by means of on-demand (just-in-time) presentation by means of pop-ups; information on how to use Boolean operators; and information on how to read a graph. To avoid split attention, this kind of information was integrated with the task environment itself (van Merriënboer & Kirschner, 2018 ). Finally, the fourth component, part-task-practice (by means of short tests) was implemented for routine aspects of research skills that should be automated, for example the formulation of a search query.

The remaining participating students ( n  = 128) completed an adapted version of the baseline online learning environment, in which autonomy, relatedness and competence support are provided. In total, need support consisted of 12 implementations (four implementations for each need), based on existing research on need support. An overview of these adaptations can be found in Tables ​ Tables1 1 and ​ and2. 2 . Although, ideally, students would work in class, under supervision of their teacher, this was not possible for all classes, due to the COVID-19 restrictions. 4 As a consequence, some students completed the learning environment partly at home. All students were supervised by their teachers (be it virtually or in class), and the researcher kept track of students’ overall activities in order to be able to contact students who did not complete the main activities. During the last two sessions of the intervention, participants submitted a two-pages long research proposal (“two-pager”). One week after the intervention, the LRST (Maddens et al., 2020a ), the ASRS (Vansteenkiste et al., 2009 ), four items related to students’ amotivation (Aydin et al., 2014 ), the value/usefulness scale (Ryan, 1982 ) and the Basic Psychological Need Satisfaction and Frustration Scale (BPNSNF, Chen et al., 2015 ) were administered in a posttest session of maximum two hours. Although most classes succeeded in organizing this posttest session in class, for some classes this posttest was administered at home. However, all classes were supervised by the teacher (be it virtually or in class). These contextual differences at the test moments will be reflected upon in the discussion section.

Adaptations online learning environment

Overview instruments

a When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively

Instruments

In this section, we elaborate on the tests used during the pretest and the posttest. Example items for each scale are presented in Appendix 1.

Motivational outcomes

In the current study, two groups of motivational outcomes are assessed: (1) students’ need satisfaction and frustration, and students’ experiences of value/usefulness; and (2) students’ level of autonomous motivation, controlled motivation, and amotivation. When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively.

The BPNSNF-training scale (The Basic Psychological Need Satisfaction and Frustration Scale, Chen et al., 2015 ; translated version Aelterman et al., 2016 5 ) measured students’ need satisfaction and need frustration while working in the learning environment, and consists of 24 items (four items per scale): (autonomy satisfaction, α  = 0.67; ω = 0.67; autonomy frustration, α  = 0.76; ω = 0.76; relatedness satisfaction, α  = 0.79; ω = 0.79; relatedness frustration, α  = 0.60; ω = 0.61; competence satisfaction, α  = 0.72; ω = 0.73; competence frustration, α  = 0.68; ω = 0.67). The items are Likert-type items ranging from one (not at all true) to five (entirely true). Although the current study focusses mainly on students’ need satisfaction, the scales regarding students’ need frustration are included in order to be able to also detect students’ potential ill-being and in order to detect potential critical issues regarding students’ needs. In addition to the BPNSNF, by means of seven Likert-type items ranging from one (not at all true) to seven (entirely true), the (for the purpose of this research translated) value/usefulness scale of the Intrinsic Motivation Inventory (IMI, Ryan, 1982 ) measured to what extent students valued the activities of the online learning environment ( α  = 0.92; ω = 0.92). Since in the research skills literature problems have been observed related to students’ perceived value/usefulness of research skills (Earley, 2014 ; Murtonen, 2005 ), and this concept is not sufficiently stressed in the BPNSNF-scale, we found it useful to include this value/usefulness scale to the study. The difference in the range of the answer possibilities (one to five vs one to seven) exists because we wanted to keep the range as initially prescribed by the authors of each instrument. All motivational measures are calculated by adding the scores on every item, and dividing this sum score by the number of items on a scale, leading to continuous outcomes. Although the IMI and the BPNSNF targeted students’ experiences while completing the online learning environment, these measures were administered during the posttest. Thus, students had to think retrospectively about their experiences. In order to prevent cognitive overload while completing the online learning environment, these measures were not administered during the intervention itself.

Students’ autonomous and controlled motivation towards learning research skills was measured by means of the Dutch version of the Academic Self-Regulation Scale (ASRS; Vansteenkiste et al., 2009 ), adapted to ‘ research skills ’. The ASRS consists of Likert-type items ranging from one (do not agree at all) to five (totally agree), and contains eight items per subscale (autonomous and controlled motivation). In the autonomous motivation scale, four items are related to identified regulation, and four items are related to intrinsic motivation. 6 In the controlled motivation scale, four items are related to external regulation, and four items are related to introjected regulation. Both scales (autonomous motivation and controlled motivation) indicated good internal consistency for the study’s data (autonomous motivation: α  = 0.91; 0.92; ω = 0.90; 0.92; controlled motivation: α  = 0.83; 0.86; ω = 0.82; 0.85). The items were adapted to the domain under study (motivation to learn about research skills). Based on students’ motivational issues related to research skills, we found it useful to also include a scale to assess students’ amotivation. This was measured with (for the purpose of the current research translated) four items related to students’ amotivation regarding learning research skills, adapted from Academic Motivation Scale for Learning Biology (Aydin et al., 2014 ) ( α  = 0.80; 0.75; ω = 0.81; 0.75). Also this measure consist of Likert-type items ranging from one (do not agree at all) to five (totally agree).

Cognitive outcomes

Students’ research skills proficiency was measured by means of a research skills test (Maddens et al., 2020a ) and a research skills task.

The research skills test used in this study is the LRST (Maddens et al., 2020a ) consisting of a combination of 37 open ended and close ended items ( α  = 0.79; 0.82; ω = 0.78; ω = 0.80 for this data set), administered via an online questionnaire. Each item of the LRST is related to one of the eight epistemic activities regarding research skills as mentioned in the introduction (Fischer et al., 2014 ), and is scored as 0 or 1. The total score on the LRST is calculated by adding the mean subscale scores (related to the eight epistemic activities), and dividing them by eight (the number of scales). In a previous study (Maddens et al., 2020a ), the LRST was checked and found suitable in light of interrater reliability ( κ  = 0.89). As the same researchers assessed the same test with a similar cohort in the current study, the interrater reliability was not calculated for this study.

In the research skills task (“two pager task”), students were asked to write a research proposal of maximum two pages long. The concrete instructions for this research proposal are given in Appendix 1. In this research proposal, students were asked to formulate a research question and its relevance; to explain how they would tackle this research question (method and participants); to explain their hypotheses or expectations; and to explain how they would communicate their results. The two-pager task was analyzed using a pairwise comparison technique, in which four evaluators (i.e. the four authors of this paper) made comparative judgements by comparing two two-pagers at a time, and indicating which two-pager they think is best. All four evaluators are researchers in educational sciences and are familiar with the research project and with assessing students’ texts. This shared understanding and expertise is a prerequisite for obtaining reliable results (Lesterhuis et al., 2018 ). The comparison technique is performed by means of the Comproved tool ( https://comproved.com ). As described by Lesterhuis et al. ( 2018 , p. 18), “the comparative judgement method involves assessing a text on its overall quality. However, instead of requiring an assessor to assign an absolute score to a single text, comparative judgement simplifies the process to a decision about which of two texts is better”. In total, 1635 comparisons were made (each evaluator made 545 comparisons), and this led to a (interrater)reliability score of 0.79. In a next step, these comparative judgements were used to rank the 218 products (15 students did not submit a two-pager) on their quality; and the products were graded based on their ranking. This method was used to grade the two-pagers because it facilitates the holistic evaluation of the tasks, based on the judgement of multiple experts (interrater reliability).

Qualitative feedback

Students’ experiences with the online learning environment were investigated in the online learning environment itself. After completing the learning environment, students were asked how they experienced the tasks, the theory, the opportunity to post answers in the forum and to ask questions via the chat, what they liked or disliked in the online learning environment, and what they disliked in the online learning environment (Fig.  1 ).

Study overview

The first research question (” Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task?” ) is answered by means of a paired samples t -test in order to look for overall improvements in order to detect potential general trends, followed by a full factorial MANCOVA, as this allows us to investigate the effectiveness for both conditions taking into account students’ pretest scores. Hence, the condition is included as an experimental factor, and students’ scores on the LRST and the two-pager task are included as continuous outcome variables. Students’ pretest scores on the LRST are included as a covariate. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariate.

The second research question (“ What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes, i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?”) ;) is answered by means of a full factorial MANCOVA. The condition (need satisfaction condition versus baseline condition) is included as an experimental factor, and students’ responses on the value/usefulness, autonomous and controlled motivation, amotivation, and need satisfaction scales are included as continuous outcome variables. ASRS pretest scores (autonomous and controlled motivation) are included as covariates in order to test the differences between group means, adjusted for students’ a priori motivation. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariates, and assumptions to be met to perform a MANCOVA are checked. 7

The third research question ( “ What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?” ), is initially answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students’ value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables. The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores, and (5) scores on the two-pager task as dependent variables. As a follow-up analysis (see ‘ results ’) two additional regression analyses are performed to look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST (6) and two-pager (7)). As the goal of this analysis is to investigate the relationships between variables as described in SDT research, this analysis focuses on the full sample, rather than distinguishing between the two conditions. An ‘Enter’ method (Field, 2013 ) is used in order to enter the independent variables simultaneously (in line with Sheldon et al., 2008 ).

The fourth research question (“ How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment?” ) is analyzed by means of the knowledge management tool Citavi. Based on the theoretical framework, students’ experiences are labeled by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. For example, students’ quotes referring to the value/usefulness of the learning environment, are labeled as ‘autonomy satisfaction’ or ‘autonomy frustration’. Students’ references towards their feelings of mastery of the learning content are labeled as ‘competence satisfaction’ or ‘competence frustration’. Students’ quotes regarding their relationships with peers and teachers are labeled as ‘relatedness satisfaction’ or ‘relatedness frustration’ (Fig.  2 ).

Overview variables

Does the deliberately designed (4C/ID based) learning environments improve students’ research skills, as measured by a research skills test and a research skills task?

Paired samples t -test. A paired samples t -test reveals that, in general, students ( n  = 210) improved on the LRST-posttest ( M  = 0.57, SD  = 0.16) compared to the pretest ( M  = 0.51, SD  = 0.15) (range 0–1). The difference between the posttest and the pretest is significant t (209) =  − 8.215, p  < 0.001, d 8  =  − 0.567. The correlation between the LRST pretest and posttest is 0.70 ( p  < 0.010).

MANCOVA. A MANCOVA model ( n  = 196) was defined checking for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariate LRST pretest did not show significant interaction effects for the two outcome variables LRST post ( p  = 0.259) and the two-pager task ( p  = 0.702). The correlation between the outcome variables (LRST post and two-pager), is 0.28 ( p  < 0.050).

Of all 233 students, 36 students were excluded from the main analysis because of missing data (for example, because they were absent during a pretest or posttest moment). These students were excluded by means of a listwise deletion method because we found it important to use a complete dataset, since, in a lot of cases, students who did not complete the pretest or posttest, did also not complete the entire learning environment. Including partial data for these students could bias the results. The baseline condition counted 86 students, and the need satisfaction condition counted 111 students. Using Pillai’s Trace [ V  = 0.070, F (2,193) = 7.285, p  ≤ 0.001], there was a significant effect of the condition on the cognitive outcome variables, taking into account students’ LRST pretest scores. Separate univariate ANOVAs on the outcome variables revealed no significant effect of the condition on the LRST posttest measure, F (1,194) = 2.45, p  = 0.120. However, a significant effect of condition was found on the two-pager scores, F (1,194) = 13.69, p  < 0.001 (in the baseline group, the mean score was 6,6/20; in the need condition group, the mean score was 7,6/20). It should be mentioned that both scores are rather low.

What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID based) learning environment fostering students’ research skills, on students’ motivational outcomes (students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?

Paired samples t -tests. The correlations between students’ pretest and posttestscores for the motivational measures are 0.67 ( p  < 0.010) for autonomous motivation; 0.44 ( p  < 0.010) for controlled motivation, and 0.38 for amotivation ( p  < 0.010). Regarding the differences in students’ motivation, three unexpected findings were observed. Overall, students’ ( n  = 215) amotivation was higher on the posttest ( M  = 2.26, SD  = 0.89) compared to the pretest ( M  = 1.77, SD  = 0.79) (based on a score between 1 and 5). The difference between the posttest and the pretest is significant t (214) =  − 7.69, p  < 0.001, d  =  − 0.524. Further analyses learn that the amotivation means in the baseline group increased with 0.65, and the amotivation in the need support group increased with 0.37. In addition, students’ ( n  = 215) autonomous motivation was higher on the pretest ( M  = 2.81, SD  = 0.81) compared to the posttest ( M  = 2.64, SD  = 0.82). The difference between the posttest and the pretest is significant t (214) = 3.72, p  < 0.001, d  = 0.254. Students’ mean scores on autonomous motivation in the baseline condition decreased with 0.19, and students’ autonomous motivation in the need support condition decreased with 0.15. Students’ ( n  = 215) controlled motivation was higher on the posttest ( M  = 2.33, SD  = 0.75) compared to the pretest ( M  = 1.93, SD  = 0.67). The difference between the posttest and the pretest is significant t (214) =  − 07.72, p  < 0.001, d  =  − 0.527. Students’ controlled motivation in the baseline group increased with 0.36, and students’ controlled motivation in the need support group increased with 0.43. However, overall, all mean scores are and stay below neutral score (below 3), indicating robust low autonomous, controlled and amotivation scores (see Table ​ Table3). 3 ). An independent samples T -test on the mean differences between these measures shows that the increases/decreases on autonomous motivation [ t (213) =  − 0.506, p  = 0.613, d  =  − 0.069] and controlled motivation [ t (213) =  − 0.656, p  = 0.513, d  =  − 0.090] did not differ between the two groups. However, the increases in amotivation [ t (213) = 2.196, p  = 0.029, d  = 0.301] does differ significantly between the two conditions. More specifically, the increase was lower in the need supportive condition compared to the baseline condition.

Mean scores and standard deviations motivational variables

a Overall, students’ ( n  = 215) autonomous motivation was significantly higher on the pretest compared to the posttest ( t (214) 3.72, p  ≤ 0.001, d  = 0.254

b Students’ (n = 215) controlled motivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 7.72, p  ≤ 0.001, d  =  − 0.527

c Students’ ( n  = 215) amotivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 07,69, p  ≤ 0.001, d  =  − 0.534)

MANCOVA. Of all 233 students, 18 students were excluded from the analysis because of missing data (for example, because they were absent during a pretest or posttest moment). Compared to the cognitive analyses, the amount of missing data is lower concerning motivational outcomes since, concerning the cognitive outcomes, some students did not complete the two-pager task. However, we found it important to use all relevant data and chose to report this is in a clear way. In total, the baseline condition counted 97 students, and the experimental condition counted 118 students. Similar to the analysis for the cognitive outcomes, a MANCOVA model was defined to check for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariates did not show significant interaction effects for the outcome variables. 9

Using Pillai’s Trace [ V  = 0.113, F (10,201) = 2.558, p  = 0.006], there was a significant effect of condition on the motivational variables, taking into account students’ autonomous and controlled pretest scores, and students’ a priori amotivation. Separate univariate ANOVAs on the outcome variables revealed a significant effect of the condition on the outcome variables amotivation, F (1,210) = 3.98, p  = 0.047; and relatedness satisfaction F (1,210) = 6.41, p  = 0.012. As was hypothesized, students in the need satisfaction group reported less amotivation ( M  = 2.38), compared to students in the baseline group ( M  = 2.18). In contrast to what was hypothesized, students in the need satisfaction group reported less relatedness satisfaction ( M  = 2.43) compared to students in the baseline group ( M  = 2.73), and no significant effects of condition were found on the outcome variables autonomous motivation post, controlled motivation post, value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, and relatedness frustration. Table ​ Table4 4 shows the correlations between the motivational outcome variables.

Correlations motivational outcome variables

AM autonomous motivation, CM controlled motivation, AMOT amotivation, VU value/usefulness, AS autonomy satisfaction, AF autonomy frustration, CS competence satisfaction, CF competence frustration, RS relatedness satisfaction, RF relatedness frustration

**Correlation is significant at the 0.010 level (2-tailed)

*Correlation is significant at the 0.050 level (2-tailed)

What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?

The third research question (investigating the relationships between students’ need satisfaction, students’ motivation and students’ cognitive outcomes), is answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables ( n  = 219). The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores ( n  = 215), and (5) scores on the two-pager task as dependent variables ( n  = 206). Table ​ Table4 4 depicts the correlations for the first three analyses. Table ​ Table5 5 depicts the correlations for the last two analyses.

Correlations motivational and cognitive outcome variables

AM  autonomous motivation, CM  controlled motivation, AMOT  amotivation, LRST  score on LRST, Twopager  score on Twopager

In Table ​ Table3, 3 , we can see that students in both conditions experience average competence and autonomy satisfaction. However, students’ relatedness satisfaction seems low in both conditions. This finding will be further discussed in the discussion section. For autonomous motivation, a significant regression equation was found F (7,211) = 37.453, p  < 0.001. The regression analysis (see Table ​ Table5) 5 ) further reveals that all three satisfaction scores (competence satisfaction, relatedness satisfaction and autonomy satisfaction) contribute positively to students’ autonomous motivation, as does students’ experienced value/usefulness. Also for students’ controlled motivation a significant regression equation was found F (7,211) = 8.236, p  < 0.001, with students’ autonomy frustration and students’ relatedness satisfaction contributing to students’ controlled motivation. The aforementioned relationships are in line with the expectations. However, we noticed that relatedness satisfaction contributed to students’ controlled motivation in the opposite direction of what was expected (the higher students’ relatedness satisfaction, the lower students’ controlled motivation). This finding will be reflected upon in the discussion section. Also for students’ amotivation, a significant regression equation was found F (7,211) = 7.913, p  < 0.001. Students’ autonomy frustration, competence frustration and students’ value/usefulness contributed to students’ amotivation in an expected way. Also for cognitive outcomes related to the research skills test, a significant regression equation was found F (3,211) = 8.351, p  < 0.001. In line with the expectations, the regression analysis revealed that the higher students’ amotivation, the lower students’ scores on the research skills test. No significant regression equation was found for the outcome variable related to the research skills task F (3,202) = 0.954, p  < 0.416. For all regression equations, the R 2 and the exact regression weights are presented in Table ​ Table6 6 .

Linear model of predictors of autonomous motivation, controlled motivation, amotivation, LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

*Significant at .050 level

As a follow-up analysis and in order to better understand the outcomes, we decided to also look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST and two-pager) by means of two additional regression analyses. The motivation behind this decision relates to possible issues regarding the motivational measures used, which might complicate the investigation of indirect relationships (see discussion). The results are provided in Table ​ Table7, 7 , and show that both for the LRST and the two-pager, respectively, a significant [ F (7,207) = 4.252, p  < 0.001] and marginally significant regression weight [ F (7,199) = 2.029, p  = 0.053] was found. More specifically, students’ relatedness satisfaction and students’ perceived value/usefulness contribute to students’ scores on the two-pager and on the research skills test. As one would expect, we see that the higher students’ value/usefulness, the higher students’ scores on both cognitive outcomes. In contrast to one would expect, we found that the higher students’ relatedness satisfaction, the lower students’ scores on the cognitive outcomes. These findings are reflected upon in the discussion section.

Linear model of predictors of LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID based) learning environment?

As was mentioned in the method section, the fourth research question was analysed by labelling students’ qualitative feedback by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. By means of this approach, we could analyse students’ need experiences in a fine grained manner. When students’ quotes were applicable to more than one code, they were labelled with different codes. In what follows, students’ quotes are indicated with the codes “BC” (baseline condition) or “NSC” (need satisfaction condition) in order to indicate which learning environment the student completed. Of all 233 students, 124 students provided qualitative feedback (44 in BC and 80 in NSC). In total, 266 quotes were labeled. Autonomy satisfaction was coded 40 times BC and 41 times in NSC; autonomy frustration was coded 13 times in BC and four times in NSC; competence satisfaction was coded 28 times in BC and 34 times in NSC; competence frustration was coded 31 times in BC and 27 times in NSC; relatedness satisfaction was coded 10 times in BC and 16 times in NSC; and relatedness frustration was coded five times in BC and 17 times in NSC. Several observations could be drawn from the qualitative data.

Related to autonomy satisfaction , in both conditions, several students explicitly mentioned the personal value and usefulness of what they had learned in the learning environment. While in the baseline condition, these references were often vague (“Now I know what people expect from me next year ”; “I think I might use this information in the future ”); some references appeared to be more specific in the need support condition (“I want to study psychology and I think I can use this information!”; “This is a good preparation for higher education and university ”; “I can use this information to write an essay ”; “I think the theory was interesting, because you are sure you will need it once. I don’t always have that feeling during a normal lesson in school”). In addition, students in both conditions mentioned that they found the material interesting, and that they appreciated the online format: “It’s different then just listening to a teacher, I kept interested because of the large variety in exercises and overall, I found it fun” (NSC).

Several comments were coded as ‘ autonomy frustration’ in both conditions. Some students indicated that they found the material “useless” (BC), or that “they did not remember that much” (BC). Others found the material “uninteresting” (BC), “heavy and boring” (NSC) or “not fun” (BC). In addition, some students “did not like to complete the assignments” (NSC), or “prefer a book to learn theory” (NSC).

Related to competence satisfaction , students in both conditions found the material “clear” (BC, NSC). In addition, students’ appreciated the example answers, the difficulty rate (“Some exercises were hard, but that is good. That’s a sign you’re learning something new” (NSC)), and the fact that the theory was segmented in several parts. In addition, students recognized that the material required complex skills: “I learned a lot, you had to think deeper or gain insights in order to solve the exercises” (NSC), “you really had to think to complete the exercises” (NSC). In the need satisfaction group, several quotes were labelled related to the specific need support provided. For example, students indicated that they appreciated the forum option: “If something was not clear, you could check your peer’s answers” (NSC). Students also valued the fact that they could work at their own pace: “I found it very good that we could solve everything at our own pace” (NSC); “good that you could choose your own pace, and if something was not clear to you, you could reread it at your own pace” (NSC). In addition, students appreciated the immediate feedback provided by the researcher “I found it very good that we received personal feedback from xxx (name researcher). That way, I knew whether I understood the theory correctly” (NSC); and the fact that they could indicate their progress “It was good that you could see how far you proceeded in the learning environment” (NSC).

In both the baseline and the need supportive condition, there were also several comments related to competence frustration . For example, students found exercises vague, unclear or too difficult. While students, overall, understood the theory provided, applying the theory to an integrative assignment appears to be very difficult: “I did understand the several parts of the learning environment, but I did not succeed in writing a research proposal myself” (NSC). “I just found it hard to respond to questions. When I had to write my two-pager research proposal, I really struggled. I really felt like I was doing it entirely wrong” (NSC)). In addition, a lot comments related to the fact that the theory was a lot to process in a short time frame, and therefore, students indicated that it was hard to remember all the theory provided. In addition, this led pressure in some students: “Sometimes, I experiences pressure. When you see that your peers are finished, you automatically start working faster.” (BC).

Concerning relatedness satisfaction , in the baseline condition, students appreciated the chat function “you could help each other and it was interesting to hear each other’s opinions about the topics we were working on” (BC). However, most students indicated that they did not make use of the chat or forum options. In the need satisfaction condition, students appreciated the forum and the chat function: “You knew you could always ask questions. This helped to process the learning material” (NSC), “My peers’ answers inspired me” (NSC), “Thanks to the chat function, I felt more connected to my peers” (NSC). In addition, students in the need satisfaction condition appreciated the fact that they could contact the researcher any time.

Several students made comments related to relatedness frustration . In both groups, students missed the ‘live teaching’: “I tried my best, but sometimes I did not like it, because you do not receive the information in ‘real time’, but through videos” (BC). In addition, students missed their peers: “We had to complete the environment individually” (BC). While some students appreciated the opportunity of a forum, other students found this possibility stressful: “I think the forum is very scary. I posted everything I had to, but I found it very scary that everyone can see what you post” (NSC). Others did not like the fact that they needed to work individually: “Sometimes I lost my attention because no one was watching my screen with me” (NSC); “I found it hard because this was new information and we could not discuss it with each other” (NSC); “I felt lonely” (NSC); “It is hard to complete exercises without the help of a teacher. In the future this will happen more often, so I guess I will have to get used to it” (NSC); “When I see the teacher physically, I feel less reluctant to ask questions” (NSC).

The current intervention study aimed at exploring the motivational and cognitive effects of providing need support in an online learning environment fostering upper secondary school students’ research skills. More specifically, we investigated the impact of autonomy, competence and relatedness support in an online learning environment on students’ scores on a research skills test, a research skills task, students’ autonomous motivation, controlled motivation, amotivation, need satisfaction, need frustration, and experienced value/usefulness. Adopting a pretest-intervention-posttest design approach, 233 upper secondary school behavioral sciences students’ motivational outcomes were compared among two conditions: (1) a 4C/ID inspired online learning environment condition (baseline condition), and (2) a condition with an identical online learning environment additively providing support for students’ autonomy, relatedness and competence need satisfaction (need supportive condition). This study aims to contribute to the literature by exploring the integration of need support for all three needs (the need for competence, relatedness and autonomy) in an ecologically valid setting. In what follows, the findings are discussed taking into account the COVID-19 affected circumstances in which the study took place.

As was hypothesized based on existing research (Costa et al., 2021 ), results showed significant learning gains on the LRST cognitive measure in both conditions, pointing out that the learning environments in general succeeded in improving students’ research skills. The current study did not find any significant differences in these learning gains between both conditions. Controlling for a priori differences between the conditions on the LRST pretest measure, students in the need support condition did exceed students in the baseline condition on the two-pager task. However, overall, the scores on the research skills task were quite low, pointing to the fact that students still seem to struggle in writing a research proposal. This task can be considered more complex (van Merriënboer & Kirschner, 2018 ) than the research skills test, as students are required to combine their conceptual and procedural knowledge in one assignment. Indeed, in the qualitative feedback, students indicate that they understand the theory and are able to apply the theory in basic exercises, but that they struggle in integrating their knowledge in a research proposal. Future research could set up more extensive interventions explicitly targeting students’ progress while writing a research proposal, for example using development portfolios (van Merriënboer et al., 2006 ).

The effect of the intervention on the motivational outcome measures was investigated. Since we experimentally manipulated need support, this study hypothesized that students in the need supportive condition would show higher scores for autonomous motivation, value/usefulness and need satisfaction; and lower scores for controlled motivation, amotivation and need frustration compared to students in the baseline condition (Deci & Ryan, 2000 ). However, the analyses showed that students in the conditions did not differ on the value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration and relatedness frustration measures. In contrast to what was hypothesized, students’ in the baseline condition reported higher relatedness satisfaction compared to students in the need supportive condition. No differences were found in students’ autonomous motivation and controlled motivation. However, as was expected, students in the need supportive conditions did report lower levels of amotivation compared to students in the baseline condition. Still, for the current study, one could question the role of the need support in this respect, as the current intervention did not succeed in manipulating students’ need experiences. In what follows, possible explanations for these findings are outlined in light of the existing literature.

Need experiences

A first observation based on the findings as described above is that the intervention did not succeed in manipulating students’ need satisfaction, need frustration and value/usefulness in an expected way. One effect was found of condition on relatedness satisfaction, but in the opposite direction of what was expected. We did not find a conclusive explanation for this unanticipated finding, but we do argue that the COVID-19 related measures at play during the intervention could have impacted this result. This will be reflected upon later in this discussion (limitations). In both conditions, students seem to be averagely satisfied regarding autonomy and competence in the 4C/ID based learning environments. This might be explained by the fact that 4C/ID based learning environments inherently foster students’ perceived competence because of the attention for structure and guidance, and the fact that the use of authentic tasks can be considered autonomy supportive (Bastiaens & Martens, 2007). However, we see that students experience low relatedness satisfaction in both conditions. The fact that the learning environment was organized entirely online might have influenced this result. While one might also partly address this low relatedness satisfaction to the COVID-19 circumstances at play during the study, this hypothetical explanation does not hold entirely since also in a previous non COVID-affected study in this research trajectory (Maddens et al., under review ), students’ relatedness satisfaction was found to be low. This finding, combined with findings from students’ qualitative feedback clearly indicating relatedness frustration, we argue that future research could focus on the question as how to provide need for relatedness support in 4C/ID based learning environments. On a more general level, this raises the question how opportunities for discussions and collaboration can be included in 4C/ID based learning environments. For example, organizing ‘real classroom interactions’ or performing assignments in groups (see also the suggestion of van Merriënboer & Kirschner, 2018 ), might be important in fostering students’ relatedness satisfaction (Salomon, 2002 ) . As argued by Wang et al. ( 2019 ), relatedness support is clearly understudied, for a long time often even ignored, in the SDT literature. Recently, relatedness is beginning to receive more attention, and has been found a strong predictor of autonomous motivation in the classroom (Wang et al., 2019 ).

Possibly, the need support provided in the learning environment was insufficient or inadequate to foster students’ need experiences. However, as the implementations were based on the existing literature (Deci & Ryan, 2000 ), this finding can be considered surprising. In addition, we derive from the qualitative feedback that students seem to value the need support provided in the learning environment. These contradictory observations are in line with previous research (Bastiaens et al., 2017 ), and call for further investigation.

Autonomous motivation, controlled motivation, amotivation

A second observation is that, in both conditions, students seem to hold low autonomous motivation and low controlled motivation towards learning research. On average, also students’ amotivation is low. The fact that students are not amotivated to learn about research can be considered reassuring. However, the fact that students experience low autonomous motivation causes concerns, as we know this might negatively impact their learning behavior and intentions to learn (Deci & Ryan, 2000 ; Wang et al., 2019 ). However, this result is based on mean scores. Future research might look at these results at student level, in order to identify individual motivational profiles (Vansteenkiste et al., 2009 ) and their prevalence in upper secondary behavioral sciences education.

A third observation is that students’ autonomous and controlled motivation were not affected by the intervention. Since the intervention did not succeed in manipulating students’ need experiences, this finding is not surprising. In addition, this is in line with Bastiaens et al.’ ( 2017 ) study, not finding motivational effects of providing need support in 4C/ID based learning environments. However, the current study did confirm that—although still higher than at pretest level, see below—students in the need supportive condition reported lower amotivation compared to students in the baseline condition. As no amotivational differences were observed at pretest level, this might indicate that students’ self-reported motivation (autonomous and controlled motivation) and/or needs do not align with students’ experienced motivation and needs. As was mentioned, this calls for further research.

Theoretical relationships

In line with previous research (Wang et al., 2019 ), multiple regression analyses revealed that students’ need satisfaction (on all three measures) contributed positively to students’ autonomous motivation. In addition, also students’ perceived value/usefulness contributed positively to students’ autonomous motivation. Students’ competence frustration and autonomy frustration contributed positively to students’ amotivation, and students’ value/usefulness contributed negatively to students’ amotivation. Students’ autonomy frustration contributed positively to students’ controlled motivation. While all the aforementioned relationships are in line with the expectations (Deci & Ryan, 2000 ; Wang et al., 2019 ), an unexpected finding is that students’ relatedness satisfaction contributed positively to students’ controlled motivation. This contradicts previous research (Wang et al., 2019 ), reporting that relatedness contributes to controlled motivation negatively. However, previous research (Wang et al., 2019 ) did find controlled motivation to be positively related to pressure . Although we did not find a conclusive explanation for this unanticipated finding, one possible reason thus is that students who contacted their peers in the online learning environment (and thus felt more related to their peers), might have experienced pressure because they felt like their peers worked faster or in a different way. Indeed, in the qualitative feedback, we noticed that some students indicated they ‘rushed’ through the online learning environment because they noticed a peer working faster. This finding calls for further research.

Overall, the results indicate that the observed need variables contributed most to students’ autonomous motivation, compared to (reversed relationships in) students’ amotivation and students’ controlled motivation. As such, when targeting students’ motivation, fostering students’ autonomous motivation based on students’ need experiences seems most promising. This is in line with previous research (Wang et al., 2019 ) reporting high correlations between students’ needs and students’ autonomous motivation, compared to students’ controlled motivation. We also investigated the relationships between students’ motivation and students’ cognitive outcomes. In line with a previously conducted study in this research trajectory (Maddens et al., under review ), but in contrast to what was hypothesized based on the existing literature (Deci & Ryan, 2000 ; Grolnick et al., 1991 ; Reeve, 2006 ) we found that nor students’ autonomous motivation, nor students’ controlled motivation contributed to students’ scores on the research skills test. However, we did find that students’ amotivation contributed negatively to students’ LRST scores. As such, when targeting students’ cognitive outcomes in educational programs, one might pay explicit attention to preventing amotivation. This is in line with previous research conducted in other domains, reporting that amotivation plays an important role in predicting mathematics achievement (Leroy & Bressoux, 2016 ), while this relationship was not found in other motivation types. Related to research skills, the current research suggests that preventing competence frustration and autonomy frustration, and fostering students’ experiences of value/usefulness might be especially promising to reach this goal.

Initially, we did not plan any analyses investigating the direct relationships between students’ needs and students’ cognitive outcomes, partly because previous research (Vallerand & Losier, 1999 ) suggests that the relationships between need satisfaction and (cognitive) outcomes are mediated by the types of motivation. To this end, we investigated the relationships between students’ needs and students’ motivation, separately from the relationships between students’ motivation and students’ cognitive outcomes. However, because of potential issues with the motivational measures (see earlier), which possibly hampers the interpretation of the relationships between students’ needs, students’ motivation, and students’ cognitive outcomes, we decided to also directly assess the regression weights of students’ needs and students’ perceived value/usefulness, on students’ cognitive outcomes. Results revealed that, in line with the expectations, students’ perceived value/usefulness contributed positively to students’ LRST scores and two-pager scores, which potentially stresses the importance of value/usefulness, not only for motivational purposes, but also for cognitive purposes. This is in line with previous research (Assor et al., 2002 ), establishing relationships between fostering relevance and students’ behavioral and cognitive engagement (which potentially leads to better cognitive outcomes). In contrast to the expectations, students’ relatedness satisfaction was found to be negatively related to students’ scores on the LRST and the two-pager. However, again, this surprising finding is best interpreted in light of the COVID-10 pandemic (see earlier).

Limitations

This study faced some reliability issues given the time frame in which the study took place. Due to the COVID-19-restrictions at play at the time of study, the study plan needed to be revised several times in collaboration with teachers in order to be able to complete the interventions. In addition, it is very likely that students’ motivation (and relatedness satisfaction) was influenced by the COVID 19-restrictions. For example, due to the restrictions, in the last phase of the intervention, students could only be present at school halftime, and therefore, some students worked from home while others worked in the classroom. In the qualitative feedback, students reported several COVID-19 related frustrations (it was too cold in class because teachers were obligated to open the windows; students needed to frequently disinfect their computers…). Also the teachers mentioned that students suffered from low well-being during the COVID-19 time frame (see further), and as such, this affected their motivation. Although all efforts were undertaken in order for the study to take place as controlled as possible, results should be interpreted in light of this time frame. The impact of the COVID-19 pandemic on students’ self-reported motivation has been established in recent research (Daniels et al., 2021 ). Overall, one could question to what extent we can expect an intervention at microlevel (manipulating need support in learning environments) to work, when the study takes place in a time frame where students’ need experiences are seriously threatened by the circumstances.

Decreasing motivation

Students’ motivation evolved in a non-desirable way in both conditions. This unexpected finding (decreasing motivation) might be explained by four possible reasons: a first explanation is that asking students to fill out the same questionnaire at posttest and pretest level might lead to frustration and lower reported motivation (Kosovich et al., 2017 ). Indeed, students spent a lot of time working in the online learning environment, so filling out another motivational questionnaire on top of the intervention might have added to the frustration (Kosovich et al., 2017 ). A second explanation is that students’ motivation naturally declines over time (which is a common finding in the motivational literature, Kosovich et al., 2017 ). A third explanation is that students, indeed, felt less motivated towards research skills after having completed the online learning environment. For example, the qualitative data indicated that a lot of students acknowledged the fact that the learning environment was useful, but that personally, they were not interested in learning the material. In addition, students indicated that the learning material was a lot to process in a short time frame, and was new to them, which might have negatively impacted their motivation. The latter (students indicating that the learning material was extensive) might indicate that students experienced high cognitive load (Paas & van Merriënboer, 1994; Sweller et al., 1994 ) while completing the learning environment. A fourth explanation is that, due to the COVID19-restrictions, students lost motivation during the learning process. A post-intervention survey in which we asked teachers about the impact of the COVID-19 restrictions on students’ motivation indicated that some students experienced low well-being during the COVID-19 pandemic, and thus, this might have hampered their motivation to learn. In addition, a teacher mentioned that COVID-19 in general was very demotivating for the students, and that students had troubles concentrating due to the fact they felt isolated. As was mentioned, the impact of COVID-19 on students’ motivation has been well described in the literature (Daniels et al., 2021 ). Although, in the current study, we cannot prove the impact of these measures on students’ motivation specifically towards learning research skills, it is important to take this context into account when interpreting the results.

Students’ learning behavior

Based on students’ qualitative feedback, we have reasons to believe that students did not always work in the learning environment as we would want them to do. Thus, students did not interact with the need support in the intended way (‘instructional disobedient behavior’: Elen, 2020 ). For example, several students reported that they did not always read all the material, did not make use of the forum, or did not notice certain messages from the researcher. However, the current research did not specifically look into students’ learning behavior in the learning environment. In learning environments organized online, future researchers might want to investigate students’ online behavior in order to gain insights in students’ interactions with the learning environment.

This study aims to contribute to theory and practice. Firstly, this study defines the 4C/ID model (van Merriënboer & Kirschner, 2018 ) as a good theoretical framework in order to design learning environments aiming to foster students’ research skills. However, this study also points to students’ struggling in writing a research proposal, which might lead to more specific intervention studies especially focussing on monitoring students’ progress while performing such tasks. Secondly, this study clearly elaborates on the operationalizations of need support used, and as such, might inform instructional designers in order to implement need support in an integrated manner (including competence, relatedness and autonomy support). Future interventions might want to track and monitor students’ learning behavior in order for students to interact with the learning environment as expected (Elen, 2020 ). Thirdly, this study established theoretical relationships between students’ needs, motivation and cognitive outcomes, which might be useful information for researchers aiming to investigate students’ motivation towards learning research skills in the future. Based on the findings, future researchers might especially involve in research fostering students’ autonomous motivation by means of providing need support; and avoiding students’ amotivation in order to enhance students’ cognitive outcomes. Suggestions are made based on the need support and frustration measures relating to these motivational and cognitive outcomes. For example, fostering students’ value/usefulness seems promising for both cognitive and motivational outcomes. Fourthly, although we did not succeed in manipulating students’ need experiences, we did gain insights in students’ experiences with the need support by means of the qualitative data. For example, the irreplaceable role of teachers in motivating students has been exposed. This study can be considered innovative because of its aim to inspect both students’ cognitive and motivational outcomes after completing a 4C/ID based educational program (van Merriënboer & Kirschner, 2018 ). In addition, this study implements integrated need support rather than focusing on a single need (Deci & Ryan, 2000 ; Sheldon & Filak, 2008 ).

Acknowledgements

This study was carried out within imec’s Smart Education research programme, with support from the Flemish government.

Appendix: Overview test instruments

• Instructions 2-pager (Maddens, Depaepe, Raes, & Elen, under review)

Write a research proposal for a fictional study.

In a Word-document of maximum two pages…

• You describe a research question and the importance of this research question
• You explain how you would answer this research question (manner of data collection and target group)
• You explain what your expectations are, and how you will report your results.

To do so, you receive 2 hours.

Post your research proposal here.

Good luck and thank you for your activity in the RISSC-environment!

Declarations

The authors declare that they have no conflict of interest.

All ethical and GDPR-related guidelines were followed as required for conducting human research and were approved by SMEC (Social and Societal Ethics Committee).

1 Fischer et al. ( 2014 ) refer to these research skills as scientific reasoning skills.

2 In Flanders, during the time of study, four different types of education are offered from the second stage of secondary education onwards (EACEA, 2018) (general secondary education, technical secondary education, secondary education in the arts and vocational secondary education). Behavioral sciences is a track in general secondary education.

3 For a complete overview on the design and the evaluation of this learning environment, see Maddens et al ( 2020b ).

4 During the time of study, the COVID-19 restrictions became more strict: students in upper secondary education could only come to school half of the time. Therefore, some students completed the last modules of the learning environment at home.

5 The BPNSNF-training scale is initially constructed to evaluate motivation related to workshops. The phrasing was adjusted slightly in order for the suitability for the current study. For example, we changed the wording ‘during the past workshop…’ to ‘while completing the online learning environment…’.

6 In the current study, we would label the items categorized as ‘intrinsic motivation’ in ASRS (finding something interesting, fun, fascinating or a pleasant activity) as ‘integration’. In SDT (Deci & Ryan, 2000 ; Deci et al., 2017 ), integration is described as being “fully volitional”, or “wholeheartedly engaged”, and it is argued that fully internalized extrinsic motivation does not typically become intrinsic motivation, but rather remains extrinsic even though fully volitional (because it is still instrumental). In the context of the current study, in which students learn about research skills because this is instructed (thus, out of instrumental motivations), we think that the term integration is more applicable than pure intrinsic motivation in self-initiated contexts (which can be observed for example in children’s play or in sports).

7 Levene’s test for homogeneity of variances was significant for the outcome “two-pager”. However, we continued with the analyses since the treatment group sizes are roughly equal, and thus, the assumption of homogeneity of variances does not need to be considered (Field, 2013 ). Levene’s test for homogeneity of variances was non-significant for all the other outcome measures.

8 Cohen’s D is calculated in SPSS by means of the formula: D = M 1 - M 2 Sp

Condition x autonomous motivation pretest Value/usefulness: p  = 0.251; autonomous motivation: p  = 0.269; controlled motivation: p  = 0.457; amotivation: p  = 0.219; autonomy satisfaction: p  = 0.794; autonomy frustration: p  = 0.096; competence satisfaction: p  = 0.682; competence frustration: p  = 0.699; relatedness satisfaction: p  = 0.943; relatedness frustration: p  = 0.870.

Condition x controlled motivation pretest Value/usefulness: p  = 0.882; autonomous motivation: p  = 0.270; controlled motivation: p  = 0.782; amotivation: p  = 0.940; autonomy satisfaction: p  = 0.815; autonomy frustration: p  = 0.737; competence satisfaction: p  = 0.649; competence frustration: p  = 0.505; relatedness satisfaction: p  = 0.625; relatedness frustration: p  = 0.741.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Aelterman N, Vansteenkiste M, Van Keer H, Haerens L. Changing teachers' beliefs regarding autonomy support and structure: The role of experienced psychological need satisfaction in teacher training. Psychology of Sport and Exercise. 2016; 23 :64–72. doi: 10.1016/j.psychsport.2015.10.007. [ CrossRef ] [ Google Scholar ]
• Assor A, Kaplan H, Roth G. Choice is good, but relevance is excellent: Autonomy-enhancing and suppressing teacher behaviours predicting students' engagement in schoolwork. British Journal of Educational Psychology. 2002; 72 (2):261–278. doi: 10.1348/000709902158883. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Aydın S, Yerdelen S, Yalmancı SG, Göksu V. Academic motivation scale for learning biology: A scale development study. Education & Science/Egitim Ve Bilim. 2014; 39 (176):425–435. doi: 10.15390/EB.2014.3678. [ CrossRef ] [ Google Scholar ]
• Bastiaens E, van Merriënboer J, van Tilburg J. Research-based learning: Case studies from Maastricht University. Springer; 2017. Three educational models for positioning the Maastricht research-based learning programme; pp. 35–41. [ Google Scholar ]
• Braguglia KH, Jackson KA. Teaching research methodology using a project-based three course sequence critical reflections on practice. American Journal of Business Education (AJBE) 2012; 5 (3):347–352. doi: 10.19030/ajbe.v5i3.7007. [ CrossRef ] [ Google Scholar ]
• Butz NT, Stupnisky RH. Improving student relatedness through an online discussion intervention: The application of self-determination theory in synchronous hybrid programs. Computers & Education. 2017; 114 :117–138. doi: 10.1016/j.compedu.2017.06.006. [ CrossRef ] [ Google Scholar ]
• Chen B, Vansteenkiste M, Beyers W, Boone L, Deci EL, Van der Kaap-Deeder J, Verstuyf J. Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation and Emotion. 2015; 39 (2):216–236. doi: 10.1007/s11031-014-9450-1. [ CrossRef ] [ Google Scholar ]
• Chi MT. Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science. 2009; 1 (1):73–105. doi: 10.1111/j.17568765.2008.01005.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cook DA, McDonald FS. E-learning: Is there anything special about the" e"? Perspectives in Biology and Medicine. 2008; 51 (1):5–21. doi: 10.1353/pbm.2008.0007. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Costa JM, Miranda GL, Melo M. Four-component instructional design (4C/ID) model: A meta-analysis on use and effect. Learning Environments Research. 2021 doi: 10.1007/s10984-021-09373-y. [ CrossRef ] [ Google Scholar ]
• Daniels LM, Goegan LD, Parker PC. The impact of COVID-19 triggered changes to instruction and assessment on university students’ self-reported motivation, engagement and perceptions. Social Psychology of Education. 2021; 24 (1):299–318. doi: 10.1007/s11218-021-09612-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• de Jong T. Scaffolds for scientific discovery learning. In: Elen J, Clark RE, editors. Handling complexity in learning environments: Theory and research. Emerald Group Publishing Limited; 2006. pp. 107–128. [ Google Scholar ]
• de Jong T, van Joolingen WR. Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research. 1998; 68 (2):179–201. doi: 10.3102/00346543068002179. [ CrossRef ] [ Google Scholar ]
• Deci EL, Eghrari H, Patrick BC, Leone DR. Facilitating internalization: The self-determination theory perspective. Journal of Personality. 1994; 62 :119–142. doi: 10.1111/j.1467-6494.1994.tb00797.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Deci EL, Olafsen AH, Ryan RM. Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior. 2017; 4 :19–43. doi: 10.1146/annurev-orgpsych-032516-113108. [ CrossRef ] [ Google Scholar ]
• Deci EL, Ryan RM. The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry. 2000; 11 (4):227–268. doi: 10.1207/S15327965PLI1104_01. [ CrossRef ] [ Google Scholar ]
• Deci EL, Ryan RM, Williams GC. Need satisfaction and the self-regulation of learning. Learning and Individual Differences. 1996; 8 (3):165–183. doi: 10.1016/S1041-6080(96)90013-8. [ CrossRef ] [ Google Scholar ]
• Earley MA. A synthesis of the literature on research methods education. Teaching in Higher Education. 2014; 19 (3):242–253. doi: 10.1080/13562517.2013.860105. [ CrossRef ] [ Google Scholar ]
• Elen J. “Instructional disobedience”: A largely neglected phenomenon deserving more systematic research attention. Educational Technology Research and Development. 2020; 68 (5):2021–2032. doi: 10.1007/s11423-020-09776-3. [ CrossRef ] [ Google Scholar ]
• Engelmann K, Neuhaus BJ, Fischer F. Fostering scientific reasoning in education: Meta-analytic evidence from intervention studies. Educational Research and Evaluation. 2016; 22 (5–6):333–349. doi: 10.1080/13803611.2016.1240089. [ CrossRef ] [ Google Scholar ]
• Field A. Discovering statistics using IBM SPSS statistics. SAGE Publications; 2013. [ Google Scholar ]
• Fischer F, Chinn CA, Engelmann K, Osborne J. Scientific reasoning and argumentation. Routledge; 2018. [ Google Scholar ]
• Fischer F, Kollar I, Ufer S, Sodian B, Hussmann H, Pekrun R, Neuhaus B, Dorner B, Pankofer S, Fischer M, Strijbos J-W, Heene M, Eberle J. Scientific reasoning and argumentation: Advancing an interdisciplinary research agenda in education. Frontline Learning Research. 2014; 4 :28–45. doi: 10.14786/flr.v2i2.96. [ CrossRef ] [ Google Scholar ]
• Grolnick WS, Ryan RM, Deci EL. Inner resources for school achievement: Motivational mediators of children's perceptions of their parents. Journal of Educational Psychology. 1991; 83 (4):508–517. doi: 10.1037/0022-0663.83.4.508. [ CrossRef ] [ Google Scholar ]
• Kosovich JJ, Hulleman CS, Barron KE. Measuring motivation in educational settings: A Case for pragmatic measurement. In: Renninger KA, Hidi SE, editors. The Cambridge handbook on motivation and learning. Cambridge University Press; 2017. pp. 39–60. [ Google Scholar ]
• Lehti S, Lehtinen E. Computer-supported problem-based learning in the research methodology domain. Scandinavian Journal of Educational Research. 2005; 49 (3):297–324. doi: 10.1080/00313830500109618. [ CrossRef ] [ Google Scholar ]
• Leroy N, Bressoux P. Does amotivation matter more than motivation in predicting mathematics learning gains? A longitudinal study of sixth-grade students in France. Contemporary Educational Psychology. 2016; 44 :41–53. doi: 10.1016/j.cedpsych.2016.02.001. [ CrossRef ] [ Google Scholar ]
• Lesterhuis M, van Daal T, van Gasse R, Coertjens L, Donche V, de Maeyer S (2018) When teachers compare argumentative texts: Decisions informed by multiple complex aspects of text quality. L1 Educational Studies in Language and Literature, 18: 1–22. 10.17239/L1ESLL-2018.18.01.02
• Maddens L, Depaepe F, Janssen R, Raes A, Elen J. Evaluating the Leuven research skills test for 11th and 12th grade. Journal of Psychoeducational Assessment. 2020; 38 (4):445–459. doi: 10.1177/0734282918825040. [ CrossRef ] [ Google Scholar ]
• Maddens L, Depaepe F, Raes A, Elen J. The instructional design of a 4C/ID-inspired learning environment for upper secondary school students' research skills. International Journal of Designs for Learning. 2020; 11 (3):126–147. doi: 10.14434/ijdl.v11i3.29012. [ CrossRef ] [ Google Scholar ]
• Maddens, L., Depaepe, F., Raes, A., & Elen, J. (under review). Fostering students’ motivation towards learning research skills in upper secondary school behavioral sciences education: the role of autonomy support.
• Martin N, Kelly N, Terry P. A framework for self-determination in massive open online courses: Design for autonomy, competence, and relatedness. Australasian Journal of Educational Technology. 2018 doi: 10.14742/ajet.3722. [ CrossRef ] [ Google Scholar ]
• Merrill MD. First principles of instruction. Educational Technology Research and Development. 2002; 50 (3):43–59. doi: 10.1007/BF02505024. [ CrossRef ] [ Google Scholar ]
• Murtonen, M. S. S. (2005). Learning of quantitative research methods: University students' views, motivation and difficulties in learning. Doctoral Dissertation.
• Niemiec CP, Ryan RM. Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education. 2009; 7 (2):133–144. doi: 10.1177/2F1477878509104318. [ CrossRef ] [ Google Scholar ]
• Pietersen C. Research as a learning experience: A phenomenological explication. The Qualitative Report. 2002; 7 (2):1–14. doi: 10.46743/2160-3715/2002.1980. [ CrossRef ] [ Google Scholar ]
• Raes A, Schellens T. Unraveling the motivational effects and challenges of web-based collaborative inquiry learning across different groups of learners. Educational Technology Research and Development. 2015; 63 (3):405–430. doi: 10.1007/s11423-015-9381-x. [ CrossRef ] [ Google Scholar ]
• Reeve J. Extrinsic rewards and inner motivation. In: Evertson CM, Weinstein CS, editors. Handbook of classroom management: Research, practice, and contemporary issues. Lawrence Erlbaum Associates Publishers; 2006. pp. 645–664. [ Google Scholar ]
• Reeve J, Jang H. What teachers say and do to support students' autonomy during a learning activity. Journal of Educational Psychology. 2006; 98 (1):209–218. doi: 10.1037/0022-0663.98.1.209. [ CrossRef ] [ Google Scholar ]
• Reeve J, Jang H, Hardre P, Omura M. Providing a rationale in an autonomy-supportive way as a strategy to motivate others during an uninteresting activity. Motivation and Emotion. 2002; 26 (3):183–207. doi: 10.1023/A:1021711629417. [ CrossRef ] [ Google Scholar ]
• Ringeisen, T., & Bürgermeister, A. (2015). Fostering students’ self-efficacy in presentation skills: The effect of autonomy, relatedness and competence support. In Stress and anxiety: Application to schools, well-being, coping and internet use , 77–87.
• Ryan RM. Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology. 1982; 43 :450–461. doi: 10.1037/0022-3514.43.3.450. [ CrossRef ] [ Google Scholar ]
• Ryan RM. Psychological needs and the facilitation of integrative processes. Journal of Personality. 1995; 63 :397–427. doi: 10.1111/j.1467-6494.1995.tb00501.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ryan RM, Grolnick WS. Origins and pawns in the classroom: Self-report and projective assessments of individual differences in children’s perceptions. Journal of Personality and Social Psychology. 1986; 50 :550–558. doi: 10.1037/0022-3514.50.3.550. [ CrossRef ] [ Google Scholar ]
• Salomon G. Technology and pedagogy: Why don't we see the promised revolution? Educational Technology. 2002; 42 (2):71–75. [ Google Scholar ]
• Schunk DH. Self-efficacy for reading and writing: Influence of modeling, goal setting, and self-evaluation. Reading & Writing Quarterly. 2003; 19 (2):159–172. doi: 10.1080/10573560308219. [ CrossRef ] [ Google Scholar ]
• Sheldon KM, Filak V. Manipulating autonomy, competence, and relatedness support in a game-learning context: New evidence that all three needs matter. British Journal of Social Psychology. 2008; 47 (2):267–283. doi: 10.1348/014466607X238797. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steingut RR, Patall EA, Trimble SS. The effect of rationale provision on motivation and performance outcomes: A meta-analysis. Motivation Science. 2017; 3 (1):19–50. doi: 10.1037/mot0000039. [ CrossRef ] [ Google Scholar ]
• Sweller J. Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction. 1994; 4 (4):295–312. doi: 10.1016/0959-4752(94)90003-5. [ CrossRef ] [ Google Scholar ]
• Vallerand RJ. Advances in experimental social psychology. Academic Press; 1997. Toward a hierarchical model of intrinsic and extrinsic motivation; pp. 271–360. [ Google Scholar ]
• Vallerand RJ, Losier GF. An integrative analysis of intrinsic and extrinsic motivation in sport. Journal of Applied Sport Psychology. 1999; 11 (1):142–169. doi: 10.1080/10413209908402956. [ CrossRef ] [ Google Scholar ]
• Vallerand RJ, Reid G. On the causal effects of perceived competence on intrinsic motivation: A test of cognitive evaluation theory. Journal of Sport Psychology. 1984; 6 :94–102. doi: 10.1123/jsp.6.1.94. [ CrossRef ] [ Google Scholar ]
• Van Merriënboer JJG, Kirschner PA. Ten steps to complex learning. Routledge; 2018. [ Google Scholar ]
• van Merriënboer J, Sluijsmans D, Corbalan G, Kalyuga S, Paas F, Tattersall C. Performance assessment and learning task selection in environments for complex learning. In: Elen J, Clark RE, editors. Handling complexity in learning environments: Theory and Research. Elsevier Science Ltd; 2006. [ Google Scholar ]
• Vansteenkiste M, Ryan RM, Soenens B. Basic psychological need theory: Advancements, critical themes, and future directions. Motivation and Emotion. 2020; 44 :1–31. doi: 10.1007/s11031-019-09818-1. [ CrossRef ] [ Google Scholar ]
• Vansteenkiste M, Sierens E, Goossens L, Soenens B, Dochy F, Mouratidis A, Beyers W. Identifying configurations of perceived teacher autonomy support and structure: Associations with self-regulated learning, motivation and problem behavior. Learning and Instruction. 2012; 22 (6):431–439. doi: 10.1016/j.learninstruc.2012.04.002. [ CrossRef ] [ Google Scholar ]
• Vansteenkiste M, Sierens E, Soenens B, Luyckx K, Lens W. Motivational profiles from a self-determination perspective: The quality of motivation matters. Journal of Educational Psychology. 2009; 101 (3):671–688. doi: 10.1037/a0015083. [ CrossRef ] [ Google Scholar ]
• Wang CJ, Liu WC, Kee YH, Chian LK. Competence, autonomy, and relatedness in the classroom: Understanding students’ motivational processes using the self-determination theory. Heliyon. 2019; 5 (7):e01983. doi: 10.1016/j.heliyon.2019.e01983. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Learning to learn: Research and development in student learning

• Published: July 1979
• Volume 8 , pages 453–469, ( 1979 )

Cite this article

• Dai Hounsell 1  

269 Accesses

15 Citations

Explore all metrics

This paper is concerned with systematic attempts to help students to learn more effectively. Current approaches to learning-to-learn, chiefly in Britain and involving groups rather than individuals, are reviewed against the background of recent research findings on student learning. Four issues are identified and discussed: contrasting conceptions of learning-to-learn; responses to the problems posed by subject and contextual varations in learning demands; the implications of autonomy, change and the individual learner; and the relationship between research on learning and the development of approaches to learning-to-learn.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

(E)Learning and What Else? Looking Back to Move Forwards

Small Steps Towards Student-Centred Learning

Learning strategies: a synthesis and conceptual model

Augstein, E. S. H. and Thomas, L. F. (1978). “Conversational Investigations of Student Learning: Methods and Psychological Tools for Learning-to-Learn”. Paper presented to the Working Party on “Student Learning”, Fourth International Conference on Higher Education, University of Lancaster, 29 August–1 September 1978.

Biggs, J. (1970). “Faculty patterns in study behaviour,” Australian Journal of Psychology 22 (2): 161–174.

Google Scholar  

Biggs, J. (1979). “Individual differences in study processes and the quality of learning outcomes,” Higher Education 8 (4): 381–394.

Brew, A. and McCormick, B. (1979). “Student learning and an independent study course,” Higher Education 8 (4): 429–441.

Buzan, T. (1974). Use Your Head . London: B.B.C.

Chibnall, B. (1979). “The Sussex Experience,” in P. J. Hills, ed., Study Courses and Counselling . pp. 37–46. Guildford: Society for Research into Higher Education.

Coles, C. R. and Fleming, W. G. (1978). “Understanding Learning: A Case Study in Student and Staff Development”. Paper presented to the 1978 Annual Conference of the Association for Programmed Learning and Educational Technology.

Da Costa, M. (1979). “Profile of a Study Skills Workshop,” in P. J. Hills, ed., Study Courses and Counselling . pp. 23–36. Guildford: Society for Research into Higher Education.

Elton, L. R. B., Hodgson, V. and O'Connell, S. (1979). “Study Counselling at the University of Surrey,” in P. J. Hills, ed., Study Courses and Counselling . pp. 47–63. Guildford: Society for Research into Higher Education.

Entwistle, N. (1978). “Knowledge structures and styles of learning: a summary of Pask's recent research”, British Journal of Educational Psychology 48 (3): 255–265.

Entwistle, N. (1979). “A Course on ‘How Students Learn’.” Paper presented to the 3rd Congress of the European Association for Research and Development in Higher Education, Klagenfurt, 2nd–6th January.

Entwistle, N., Hanley, M. and Hounsell, D. (1979). “Identifying distinctive approaches to studying,” Higher Education 8 (4): 365–380.

Garfield, L. and McHugh, E. A. (1978). “Learning counselling: a higher education student support service,” Journal of Higher Education 49 (4): 382–392

Gibbs, G. (1977a). “Can students be taught how to study” Higher Education Bulletin 5 (2): 107–118

Gibbs, G. (1977b). Learning to Study: A Guide to Running Group Sessions . Milton Keynes: The Open University, Institute of Educational Technology, Tuition and Counselling Research Group.

Gibbs, G. (1978). “Intervening in Student Learning — A Practical Strategy.” Paper presented to the Working Party on Student Learning, Fourth International Conference on Higher Education, University of Lancaster, 29 August–1 September 1978.

Gibbs, G. and Northedge, A. (1977). “Learning to study — a student centred approach,” Teaching at a Distance 8: 3–9.

Goldman, G. (1979). “A Contract for Academic Improvement.” in P. J. Hills, ed., Study Courses and Counselling . pp. 64–74. Guildford Society for Research into Higher Education.

Helweg-Larsen, B. (1977). “Thoughts on propagating study skills,” Impetus 7: 11–19.

Hills, P. J., ed., (1979). Study Courses and Counselling: Problems and Possibilities . Guildford: Society for Research into Higher Education.

Hills, P. J. and Potter, F. W. (1979). “Group Counselling and Study Skills” in P. J. Hills, ed., Study Courses and Counselling . pp. 13–22. Guildford: Society for Research into Higher Education.

Hounsell, D. (1979). “Learning to Learn: A Critical Introduction to the Work of Graham Gibbs and Andrew Northedge.” Paper presented to the 3rd Congress of the European Association for Research and Development in Higher Education, Klagenfurt, 2nd–6th January.

Howe, M. J. A. (1976): “Good learners and poor learners,” Bulletin of the British Psychology Society 29: 16–19

Kelly, G. (1955). A Theory of Personality: The Psychology of Personal Constructs . New York: Norton.

Laurillard, D. (1979). “The processes of student learning,” Higher Education 8 (4): 395–409.

Learning Methods Group (1978) Intensive 6-Day Courses of Advanced Studying Skills. (Brochure). Learning Methods Group, 84 Hampstead Way, London NW11.

Marton, F. (1975). “What Does It Take to Learn?” in N. Entwistle and D. Hounsel, eds., How Students Learn . pp. 125–138 Lancaster: University of Lancaster, Institute for Research and Development in Post Compulsory Education.

Marton, F. and Säljö, R. (1976). “On qualitative differences in learning: I — Outcome and process,” British Journal of Educational Psychology 46: 4–11.

Nisbet, J. (1979). “Beyond the Study Methods Manual.” in P. J. Hills, ed., Study Courses and Counselling . pp. 6–12. Guildford Society for Research into Higher Education.

Northedge, A. (1975). “Learning through discussion at the Open University”, Teaching at a Distance 2: 10–19.

Pask, G. (1976). “Styles and strategies of learning,” British Journal of Educational Psychology 46: 128–148.

Pask, G. (1977). Learning Styles, Educational Strategies and Representations of Knowledge: Methods and Applications . Progress Report 3 to the Social Science Research Council on Research Programme HR 2708/1. Richmond: Systems Research Ltd.

Perry, W. G. (1959). “Students' use and misuse of reading skills,” Harvard Education Review 29 (3): 193–200.

Perry, W. G. (1970). Intellectual and Ethical Development in the College Years: A Scheme . New York: Holt, Rinehart & Winston

Perry, W. G. (1977). “Of study and the student,” Higher Education Bulletin 5 (2): 120–124

Ramsden, P. (1979). “Student learning and perceptions of the academic environment,” Higher Education 8 (4): 411–427.

Rogers, C. M. (1969). Freedom to Learn . Columbus, Ohio: Merrill.

Roueche, J. E. and Snow, J. J. (1977). Overcoming Learning Problems: A Guide to Developmental Education in College . San Francisco: Jossey-Bass.

Smith, M. (1978). “Teaching to Learn?” Studies in Higher Education 3 (2): 221–5.

Säljö, R. (1979). “Learning about learning,” Higher Education 8 (4): 443–451.

Thomas, L. F. and Augstein, E. S. H. (1976). The Self-Organised Learner and the Printed Word . Final Progress Report to the Social Science Research Council. Uxbridge: Brunel University, Centre for the Study of Human Learning.

Wankowski, J. (1978). [Comments on Gibbs' approach to learning-to-learn]. Teaching News (University of Birmingham), 4: 10.

Watts, I. (1810). “The Improvement of the Mind,” in The Complete Works of Isaac Watts Vol. 2. London: Barfield. (Cited in Nisbet, 1979, p. 7).

Download references

Author information

Authors and affiliations.

Institute for Research and Development in Post-Compulsory Education, University of Lancaster, Lancaster, UK

Dai Hounsell

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

About this article

Hounsell, D. Learning to learn: Research and development in student learning. High Educ 8 , 453–469 (1979). https://doi.org/10.1007/BF01680535

Download citation

Issue Date : July 1979

DOI : https://doi.org/10.1007/BF01680535

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Research Finding
• Student Learning
• Current Approach
• Individual Learner
• Systematic Attempt
• Find a journal
• Publish with us
• Track your research

STEAM education: student learning and transferable skills

Journal of Research in Innovative Teaching & Learning

ISSN : 2397-7604

Article publication date: 27 April 2020

Issue publication date: 24 June 2020

Globally, interdisciplinary and transdisciplinary learning in schools has become an increasingly popular and growing area of interest for educational reform. This prompts discussions about Science, Technology, Engineering, Arts and Mathematics (STEAM), which is shifting educational paradigms toward art integration in science, technology, engineering and mathematics (STEM) subjects. Authentic tasks (i.e. real-world problems) address complex or multistep questions and offer opportunities to integrate disciplines across science and arts, such as in STEAM. The main purpose of this study is to better understand the STEAM instructional programs and student learning offered by nonprofit organizations and by publicly funded schools in Ontario, Canada.

Design/methodology/approach

This study addresses the following research question: what interdisciplinary and transdisciplinary skills do students learn through different models of STEAM education in nonprofit and in-school contexts? We carried out a qualitative case study in which we conducted interviews, observations and data analysis of curriculum documents. A total of 103 participants (19 adults – director and instructors/teachers – and 84 students) participated in the study. The four STEAM programs comparatively taught both discipline specific and beyond discipline character-building skills. The skills taught included: critical thinking and problem solving; collaboration and communication; and creativity and innovation.

The main findings on student learning focused on students developing perseverance and adaptability, and them learning transferable skills.

Originality/value

In contrast to other research on STEAM, this study identifies both the enablers and the tensions. Also, we stress ongoing engagement with stakeholders (focus group), which has the potential to impact change in teaching and teacher development, as well as in related policies.

• STEM and arts
• STEM and creativity
• Art integration
• Integrated curriculum
• Art-based curriculum
• STEAM and Canada
• Transferrable skills
• Transdisciplinary
• 21st century skills
• Domain-general skills
• Workplace skills

Bertrand, M.G. and Namukasa, I.K. (2020), "STEAM education: student learning and transferable skills", Journal of Research in Innovative Teaching & Learning , Vol. 13 No. 1, pp. 43-56. https://doi.org/10.1108/JRIT-01-2020-0003

Emerald Publishing Limited

Copyright © 2020, Marja G. Bertrand and Immaculate K. Namukasa

Published in Journal of Research in Innovative Teaching & Learning . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Globally, interdisciplinary and transdisciplinary learning in schools has become an increasingly popular and growing area of interest for educational reform. This prompts discussions about Science, Technology, Engineering, Arts and Mathematics (STEAM), which is shifting educational paradigms toward art integration in science, technology, engineering and mathematics (STEM) subjects. According to Reeves et al. (2004) , learning opportunities for students should include “authentic tasks” set in a real-world context. Authentic tasks consist of ill-defined problems, complex or multistep questions, multiple ways to approach a problem and subtasks that integrate across disciplines ( Armory, 2014 ). The main purpose of this study is to better understand the learning that results from STEAM instructional programs. This study has implications for designing and teaching learning tasks in STEAM programs. This study addresses the research questions: what interdisciplinary and transdisciplinary skills do students learn from engaging in STEAM programs offered by nonprofit organizations and by publicly funded schools? What are students observed to learn when they engage in tasks offered in these programs?

Curriculum models and the transdisciplinary approach to STEAM

Industrial, political and educational leaders rally behind initiatives that support the development of students' workforce competencies, such as by “‘promoting deeper' learning through skills such as problem solving and collaboration” ( Allina, 2018 , p. 80). STEM and STEAM education scholars agree that STEAM initiatives enable students to transfer their knowledge across disciplines and thus to creatively solve problems in a different context, both in the classroom and out-of-school ( Gess, 2017 ; Liao, 2016 ). According to Hughes (2017) , students need these character-building or transferable skills: “students need to develop and apply for successful learning, living and working” (p. 102). STEAM teaches students skills such as “critical thinking and problem solving; collaboration and communication; and creativity and innovation” ( Liao et al. , 2016 , p. 29) that can be transferred to another context. Transdisciplinary approaches to STEAM education are highly valued by both the teacher and the student because they allow the student to view the problem or design process from multiple angles or different perspectives that can be applied to a real-world context ( Costantino, 2018 ). Empirical research on STEAM education, however, is in its infancy and little research has compared more than two STEAM programs or models. Our research compares four STEAM programs and focuses particularly on the nature and learning outcomes of models of STEAM education in those programs.

Theoretical framework

The theoretical frameworks adopted for this study are multilayered to analyze three levels: task design, STEAM models and interdisciplinary learning experiences. For the level of task design, we adopt the “low floor, high ceiling, wide walls” lens. Gadanidis (2015) utilizes this term to describe learning environments when designing and implementing tasks that integrate mathematics and coding in the classroom. The goal of the tasks he designs is to enhance the students' overall learning experience and make it more meaningful through curiosity and creativity. This learning environment provides multiple entry points, multiple ways to approach a problem and multiple representations of these activities, so that students of all ages and abilities can participate ( Gadanidis et al. , 2011 ). To analyze pedagogy, curriculum and instruction models in the four STEAM programs we take into account critical work by previous researchers. A critical lens has been adopted by researchers such as Blikstein (2013) to critique efforts that limit students' engagement on interdisciplinary learning tasks such as surface or basic learning of how to use technology tools and skills. Kafai et al. (2019) support adopting frameworks that cross boundaries and focus on cognitive skills, social participation, critical-social justice approaches and on learning using computer technology. According to Blikstein (2013) , educators should avoid “quick demonstration projects” that are aesthetically pleasing to the students but require little effort. Instead they should promote “multiple cycles of design” so that students create complex solutions and products, design “powerful interdisciplinary projects” that narrow the gap between disciplines, “contextualize the learning in STEM [/STEAM]”. This makes abstract concepts more meaningful and engaging, and generates an “environment that values multiple ways of working” (p. 18). Thirdly, we use three of Kolb and Kolb's (2005) guiding principles of experiential learning theory as a framework to analyze the interdisciplinary and transdisciplinary student learning in the STEAM programs. The main guiding principles of experiential learning theory according to Kolb and Kolb (p. 3) are the following: learning is best conceived as a process, learning is a holistic process of adaptation to the world and learning is the process of creating knowledge. Kolb and Kolb's framework resonates with Papert's work. Papert's (1980) constructionism theory of learning is foundational to Maker education, which is guiding the adoption of the broader Maker culture and makerspaces ( Halverson and Sheridan, 2014 ) in schools. Kolb and Kolb's work also resonates with the emphasis on the processes developed in design-based learning and the learning of transferable skills.

Research design

This research was a qualitative case study. According to Yin (2004) , a case study focuses on a bounded-system and sheds light on a situation. The main purpose of a case study is to focus on a particular phenomenon, such as a process, event, person or other area of interest ( Gall et al. , 2007 ). A collective case study ( Stake, 2005 ), in which the researcher selects more than one representative case, enables more theoretical generalizations ( Cousin, 2005 ).

We took a sample of four different STEAM programs in Ontario, Canada, two nonprofit organizations and two in-school research sites, with a total of 103 participants, 19 adults and 84 students. We collected data from document analyses, observations and interviews. The lead author observed the participants during the lessons. She also conducted conversational interviews using open-ended questions ( Arthur et al. , 2012 ). Table 1 summarizes the settings of the research sites and the environment. At each of the research sites three to eight classes or sessions were observed. Most of the classes observed, apart from In-School 1, depended upon the teacher/instructor's availability. The curriculum documents analyzed consisted of course and program overview, collaborative meeting notes, unit plans and lesson plans for each of the sites. The data analyzed included: interview transcripts, observation data written by one of the researchers and analysis of curriculum document photocopies. A focus group discussion was also conducted with four elementary classroom teachers. At this discussion, one of the researchers presented preliminary results on the curriculum and instructional models of STEAM. The lead researcher then orchestrated discussion on how classroom teachers viewed such models as meeting their goals. The focus group discussion was audio recorded, transcribed and analyzed.

This paper presents the research results from the analysis of observation data, interview transcripts, curriculum document photocopies and focus group transcripts.

Student learning and transferable skills

Interviewer: What would you say are the learning objectives for this STEAM program?

Teacher Librarian: I'm all about giving them skills to express their ideas, transferable skills so they can take with them to the next grade level. Keep practicing those skills, keep developing those skills and hopefully bring some of those skills together in unconventional ways.

Similarly, the director at Non-Profit 1 wanted his students to “look at the world around them as the place that can be changed by their ideas . . . [and] make this city [world] a better place somehow.” At Non-Profit 2, instructor 2 explained that “giving them the tools to have a better life essentially and work life, that's where adding technology and adding these new features, new STEAM learning comes from.” The director, instructors and teachers are empowering the students to make a difference in their community and the world. The director of the STEAM program said, “what we are trying to do is to empower people [kids] to feel like . . . they can make a difference in the world” (Non-Profit 1). The findings suggest that, by teaching these character-building skills, the instructor/teacher can empower these students to solve real-world problems, to have more opportunities in the future and to have an impact on the world.

The analysis of the curriculum documents revealed that those documents of the in-school research sites were more detailed and aligned with specific standards in the Ontario curriculum than those of the nonprofit sites, which were less detailed and not tightly based on the curriculum standards.

All sites included an initial stage that built on students' curiosity and interest in the lesson or session.

Both nonprofit cases used games and storytelling to pique the interest and curiosity of their students at the beginning of an activity. At Non-Profit 1, the director explained that “the first stage is play so that they can experiment with the technology [to] get an idea of what it can do, [and] get excited about it.” At Non-Profit 2, students were given the opportunity by the instructors to tinker and play with the craft materials and technologies to spark their interest and curiosity as they researched, designed and created objects. For example, students played with an apparatus made out of Popsicle sticks and syringes in which they learned how changes in pressure can make the contraption move.

In contrast, both in-school cases used inquiry-type questions to get students to wonder, and to stir their imagination and pique their curiosity at the beginning of an activity. In the post-observation interview, the special education teacher expressed that the “inspiring piece [is] . . . doing these type of learning activities . . . you are activating kids' natural curiosity, their natural interest in figuring out how things work and how they can make things better” (In-School 2). Both in-school cases allowed students the opportunity to tinker as they explored a new technology before using it to solve a problem or to create a digital or concrete object, such as a robot or a multimedia work of art.

Oral communication

All sites included opportunities for students to discuss their making processes verbally.

Non-Profit 1 and 2 facilitated group discussions with their students and prompted them to answer inquiry-type questions as a class. Non-Profit 1 also provided students with several opportunities to communicate their ideas verbally. Students used oral communication skills when discussing the features of their product in a video commercial or when sharing what they learned about the design of their product in a video presentation.

At the in-school research sites, students documented their “making process” of the prototype and expressed their thoughts verbally. At In-School 1, the students documented every stage of the making process in a video to capture their observations, creations and group discussions. The teacher librarian commented that the intent of the documentation was to “drive their thinking forward,” and this documentation appeared to deepen the students' understanding as they reflected on, articulated and then shared their thoughts and ideas.

Written communication

The two nonprofit sites provided students with the opportunity to communicate their ideas in writing at different stages of the making process.

Non-Profit 1 clearly indicated specific tasks in their lesson plans where students communicated their ideas in writing. For example, when coding in the visual programming language Scratch, students were asked to write a story by creating a plan and a sequence of events for their characters. During the planning stage of their projects, students sketched their ideas and expressed their thoughts through writing and drawing as seen in Plate 1 . Non-Profit 2, similarly, allowed their students the freedom to make a plan or sketch their ideas and prompted them to use multiple media. For example, some students wrote out their plan, while others designed them digitally, or used modeling clay to create their 3D figures.

In-School 1 encouraged students to document the making process by writing, and completing a handout provided by the teacher librarian. The handout provided the following writing prompts: to write their answer to the inquiry questions about the activity, to write notes resulting from their Internet search and to write out a plan for their design (as seen in Plate 2 ). In-School 2 used nontraditional ways of getting students grades 1–3 to write, which included using sticky notes and index cards. The teacher librarian then encouraged the students to further organize and review their ideas by articulating their thoughts into categories and subcategories. At In-School 2, the Grade 5 students were, specifically, prompted to complete a log during the design-inquiry lesson. During this lesson, the students were given a hand-out, which documented every stage of the design-inquiry process, to complete. It appeared that the two in-school cases provided students with more opportunities to communicate in written form and share their thinking since students were given a handout and student log to record their ideas and thoughts, as seen in Plate 2 . In contrast, Non-Profit 2 instructors did not explicitly mention in the curriculum documents or during the lessons observed that students should document or write, but allowed their students the freedom to make a plan or sketch their ideas using multiple media, such as writing, modeling (e.g. clay) and/or designing them digitally.

Perseverance and adaptability

At all sites the adults interviewed spoke about how they engaged students in specific activities to develop perseverance.

At Non-Profit 1, the instructors used picture books to get kids (6–9 years old) to discuss selected transferable skills such as adaptability and persistence. These picture books allowed students to visually understand the skills and to discuss their views such as on their experiences where these skills could have been helpful. Students, for example, discussed their views on making mistakes. The instructor at Non-Profit 1 said she wanted her students to “not be afraid of making mistakes and trying new things.” When asked “what type of curriculum or instructional models do you commonly use in the STEAM lab/center?”, the director and instructor at Non-Profit 1 mentioned that they created a learning environment where failure and iteration were built into the lesson or session.

To develop perseverance among students both nonprofit and in-school cases got students to plan, design, make a prototype, test, redesign and, when the prototype did not work, to repeat the design-inquiry process (see Plate 3 ). At the in-school and nonprofit sites, 12 out of 15 adult participants mentioned perseverance during the interviews. For example, when a teacher librarian was asked what the students learned she answered, “developing mindsets, developing perseverance and grit in an openness to try new things” (In-School 2). The teacher librarian at In-School 1 talked about the goal to “grow persistence and [to] keep a positive frame of mind.” Similarly, a Grade 5 teacher mentioned that he “saw a lot of [perseverance]. . . and problem solving even with robotics, they had to code the robot to move around a shape and to escape the maze through using trial and error and you know they had to keep going and not give up” (In-School 1).

Collaboration

Both nonprofit cases encouraged students to collaborate and work as a team when they were given group challenges. For example, in the spaghetti challenge, students had to build the tallest free-standing structure using spaghetti, and in the class mascot challenge students had to design an original mascot character for their team using wood and the laser cutter (seen in Plate 4 ). The two in-school sites provided students with the opportunity to work collaboratively in groups on a project or on a mini-assignment that took more than one day to complete. In contrast, the group challenges at the nonprofit sites were used as a team-building activity in which students were given a limited amount of time and resources to complete the task. For example, Non-Profit 2 gave the students specific constraints, such as 40 sticks of spaghetti, 5 marshmallows, 1 strip of tape and 10 min, to complete the spaghetti challenge. In the interview, the director at Non-Profit 1 explained that their goal was to teach the students “personal skills . . . which are collaboration, knowledge about themselves, . . . [knowledge] about their own personal strengths and challenges” so they can effectively work as a team.

The in-school STEAM programs provided students with several opportunities to work in groups whether they were designing a robot, creating a pattern in Minecraft, programming a robot such as LEGO EV3, Ozobot or Sphero to move around a perimeter or move to the beat of a song. At In-School 1, a Grade 2 teacher expressed that she “think[s] that collaboration is absolutely key.” A Grade 5 teacher found that when kids did not know what to do “after they explore[d] and [then were given opportunities to] collaborate with their own teammates . . . they would create these amazing things” (In-School 1).

Critical thinking

Non-Profit 1 was not as concerned with the product as much as the process. The director said that one of the student learning objectives “is critical thinking, so that they can make a plan . . . and critically analyze [their] plan to make sure that it is awesome and doable, so the design always comes before the building” (Non-Profit 1). At Non-Profit 2, students were given various tasks that would prompt them to use critical-thinking and problem-solving skills. For example, when Grade 7 and 8 students were creating conditional (if-then) statements in a programming language for novices such as Scratch or Java script, they would have to use problem-solving skills to write the code and critical-thinking skills to check for errors (debug) in their program when it was unsuccessful.

At the in-school sites, the learning objectives for two of the STEAM disciplines, science and mathematics, appeared to enhance students' opportunities to use critical-thinking and problem-solving skills. Each lesson at In-School 2 focused on a question or set of questions that prompted students to brainstorm and think about a real-life context, such as “How might we get Georgie [the robot] home and describe the path?” Students were given the opportunity to answer questions such as this one using multiple approaches. Further, students used unplugged methods (e.g. methods with no digital or screen technology, such as string stories, drawings, LEGO creations and arrow diagrams), as seen in Plate 5 , to focus their minds on and solve selected problems. In this example, Kindergarten and Grade 1 students had to think critically about direction, measurement, angles and scale factor and the distances that were represented on the path they defined for the robot. These students also used different digital technologies, such as Ozobots and Beebots, to code and enact the path that they had described as Georgie's path home. Thus, these students had to further use problem-solving skills to transfer their unplugged solution to the solution simulated by programming a robot to follow a specific path.

Summary of student learning and transferable skills

Every research site encouraged the students to tinker and experiment with the technology through play and discovery. During our observations, all students learned character-building skills that were exemplified in the curriculum documents, such as curiosity and imagination, oral and written communication, perseverance and adaptability, collaboration, and critical thinking and problem-solving. Specifically, Non-Profit 1 and In-School 2 used storytelling and answering inquiry-type questions to engage their students and to activate the students' natural curiosity. Non-Profit 1 and 2 used games to fuel the students' interest, imagination and curiosity. Both in-school cases also used the Ontario curriculum when creating some of the specific objectives and inquiry-type questions. Non-Profit 1 and both in-school cases, 3 of 4 sites, chose to document the “making process” through video. This allowed students to communicate and share their thinking. The two in-school cases allowed students to both share their thinking verbally in a video and in writing in a student log. The purpose of documenting the “making process” was to drive students thinking forward by reflecting on what worked well, what needed to be changed and what could have been done differently.

At the nonprofit and in-school sites, students learned to develop persistence and adaptability when going through the design-inquiry process of plan–design–make–test–redesign and repeat. At Non-Profit 1, the director and instructor created a learning environment in which students were not afraid to make mistakes. To encourage perseverance, failure and iteration were built into the lesson or session at Non-Profit 1. All four research sites created group activities and encouraged students to collaborate with one another, whether students were working on a team challenge or a group project. Through collaboration, students learned their strengths and “after they explore[d] and collaborate[d] with their own teammates and then they would create these amazing things” (Grade 5 Teacher, In-School 1). These character-building skills were also mentioned in the curriculum documents and were “all about giving [students] skills to express their ideas, transferable skills” that can be used in a different context or to solve a different problem.

Classroom teachers' views on student learning and transferable skills

Well we're preparing them for a better world. The world I grew up in was a factory world. Some of my fellow students went to jobs where they would do the same job every day for the rest of their lives and that's not the case anymore . . . I really like the authentic experiences and the rich tasks. I think that in our world today there are a lot of problems to be solved.

Whether it regards sustainability or you know just, compassion in the world, solving some of these food and hunger issues, water resources issues and I think that preparing our students to connect with their learning is a viable skill that they can take with them in the future. You know [for example, collaboration and communication skills] where there are so many different entry-level projects and contests [in these STEAM learning activities], where students are really creating things that are being used in our community and are being used to solve real-world problems. And I think that's when I find my kids the most engaged when they can actually see that thinking.

During the focus group discussion, teachers identified challenges they face when developing some of the character-building skills. For example, Teacher B described one of her challenges as “growth mindset [perseverance]. . . That's one of the biggest challenges when we're doing STEAM activities . . . it's like an unwillingness to try again or change the design even if it's not working.” Teacher D suggested “that's why I think that it needs to start in the younger years and this idea of building, designing and trying again, being resilient, knowing how many prototypes something takes before [you get the final product] in the real world . . . You are never going to get a final product without going through that messy process of try-fail-start again” and repeat. This idea of failure and reiteration of a lesson seemed to resonate with the focus group participants. They all knew that it was important for student learning and was built into both the design-inquiry process and the STEAM activities at the research sites.

At all the research sites, students learned character-building skills. These skills seemed transferable because they could be used in real life: in high school, in post-secondary education and, eventually, in the workforce. When the teachers were asked “what are some of the greatest benefits in STEAM education?”, they saw the benefits of how the STEAM tasks connected to students' real lives, to the world in which students find themselves, and to how students may prepare for future jobs. A Grade 5 teacher at In-School 1 said “I think the biggest thing is it just speaks to kids; this is their language right now. This is their world if you think about like future job opportunities, this is like 21 st Century learning for kids, this is what they know and what they are interested in.”

Instructor 2 at Non-Profit 2 said “giving them the tools to have a better life essentially and work life, that's where adding technology and adding these new features, new STEAM learning comes from.” The director at Non-Profit 1 wanted his students to “think about, think of, look at the world around them as the place that can be changed by their ideas . . . [and] make this city a better place somehow.” Teachers (and students in their interviews) in the STEAM programs considered the skills being learned as valuable and realistic. The director of the STEAM program said “what we are trying to do is to empower [kids] to feel like they can have control over their lives, they can make things that they want, … that they need. They can make a difference in the world and these tools of technology and science and engineering are really a great way to do that” (Non-Profit 1).

Our main finding on student learning in this study focused on students developing perseverance and adaptability, and character-building skills such as: curiosity and imagination, oral and written communication, collaboration, and critical thinking and problem-solving.

One of the main character-building skills mentioned during the interviews was perseverance. The instructors/teachers encouraged students to make mistakes and take risks. The students' learning experience, the “making process” as well as the product made were important in each STEAM program. Students documented the “making process” and shared their thinking through presentations, written documentation, photos and videos at Non-Profit 1 and at both in-school sites.

The findings also support Conley et al. 's (2014) claims that integrating the arts into STEM promotes communication and critical-thinking skills, and it helps students to develop a global perspective.

Perseverance, adaptability, failure and iteration

At the non-profit and in-school sites, students appeared to learn and practice perseverance and adaptability when going through the design-inquiry process of plan–design–make–test–redesign and repeat. The teacher librarian at In-School 2 said that one of the greatest benefits of STEAM was “developing mindsets, developing perseverance and grit in an openness to try new things.” She explains “I think that's one of the things that we're trying to build is perseverance and risk taking and grit and … it's more about the learning . . . [and] the learning is more about the process” (In-School 2). Encouraging students to persevere by taking risks, making mistakes, and by developing grit and resilience was evident in all the STEAM programs we studied. We observed that at all the nonprofit and in-school sites, the instructors/teachers also seemed to create an environment in which students felt comfortable making mistakes and taking risks because students had a positive teacher–student relationship. This appeared to be unrestricted (e.g. not restricted to a specific time or place) when the students were asking questions and interacting with the teacher.

Transferable skills

At all the research sites, students learned character-building skills (21st century skills) which were “transferable skills so they can take [it] with them to the next grade level” and use those skills in another context (teacher librarian, In-School 1). The findings on students learning skills that are transferrable is in line with the literature on the benefits of STEAM learning; in STEAM education students are able to transfer their knowledge across disciplines and creatively solve problems in another context ( Gess, 2017 ; Liao, 2016 ).

Industrial, political and educational leaders rally for students to develop workforce competencies by “‘promoting deeper’ learning through skills such as problem solving, critical thinking, and collaboration” ( Allina, 2018 , p. 80). A Grade 5 teacher at In-School 1 echoed this by saying “this is like 21st Century learning for kids.” According to Hughes (2017) , students need these character-building skills to “develop and apply for successful learning, living and working” (p. 102). The STEAM programs in this study teach character-building skills, such as “critical thinking and problem solving; collaboration and communication; and creativity and innovation” ( Liao et al. , 2016 , p. 29) that can be transferred to another context, such as in the home, in high school, in post-secondary education and in the workforce.

Politicians and industry leaders tend to focus on the academic skills and career paths of students whereas in the STEAM programs in this study the instructors/teachers valued the process and the character-building skills that students developed. The findings are in line with Kolb and Kolb's (2005) guiding principle of the experiential learning theory which states that learning is best conceived as a process. For example, students were given the opportunity to document the making process to develop a deeper understanding. The focus on developing students' perseverance, collaborative and critical thinking skills is in line with Blikstein's (2013) assertion that if “the aim is efficiency . . . it could have undermined students' willingness to persist through difficult problems” (p. 15) or could encourage them to “prematurely [abort] design elements that they deemed too difficult” (p. 14). In these STEAM activities students were encouraged to persevere by taking risks, making mistakes, and by developing the grit to persevere on multistep tasks. All of the lessons and units studied by the researchers appeared to be student-centered and to incorporate student interests. For example, the activities started with “low floor” entry-level questions such as those that made students curious or in which they wrote about their design plans. In addition, the activities appeared to be “high ceiling” as students moved on to fabricate, program, solder and wire their designs. The activities were also “wide walls” because they allowed multiple ways to approach a problem and encouraged both student creativity and innovation ( Gadanidis et al. , 2011 ; Gadanidis, 2015 ).

In this paper, we highlight the findings from the interviews, observations, curriculum documents and the focus group as well as the cross-case findings among the different data sources. This study has implications for future research such as investigating the design and implementation of STEAM programs that promote the teaching and learning of workplace and transferable skills. Although the findings provide deeper insight into STEAM education, we offer several possibilities for future research. This study provides a snapshot of the STEAM programs, in which the data were collected over four months. In order to provide even more insight into this phenomenon of STEAM education there need to be more research sites, and data that are collected over a longer period of time. Specifically, we need to study how these character-building skills transfer to other contexts and different subject areas over time. Educators, researchers and policymakers have an invested interest in assessment and documentation; it would also be beneficial to gain more insight on how educators assess and document student learning in these STEAM programs.

The scope of this paper focused mainly on the character-building skills, but the STEAM curriculum also provided students with the opportunity to learn academic skills. The instructors/teachers focused on providing students with the opportunity to engage in rich tasks and authentic experiences. The STEAM programs and activities extended students' engagement beyond simple and quick explorations of robots, programming software and fabrication tools, could be attributed to these nonprescriptive settings (i.e. nonclassroom contexts timetabled for a single STEAM subject and/or makerspace environment). The findings support Blikstein's (2013) claim that educators should avoid “quick demonstration projects” and instead promote “multiple cycles of design” through “powerful interdisciplinary projects” (p. 18) that encourage students to transfer their knowledge across disciplines and solve problems in another context ( Gess, 2017 ; Liao, 2016 ). The setting of the in-school STEAM programs in the library learning commons (e.g. makerspace) or in the after-school program, in particular, outside the constraints of single-subject specific lesson, specific curriculum standard and expectations, concept or discipline, appeared to enhance the students' overall learning experience, making the experience deep and more meaningful. For educators, researchers and policymakers, the goal should be to seek to provide STEAM learning experiences in classrooms for all learners. This would encourage students to engage in and learn, even if occasionally, in ways that transcend their knowledge across individual disciplines and teach them domain-specific, domain-general/interdisciplinary and other transdisciplinary learning skills.

At Non-Profit 1, students expressed their thoughts through writing and drawing to describe the robot's functions

At In-School 1, students wrote information in the collecting Ideas section to answer the inquiry-type questions that would help them build and program their robot

At Non-Profit 2, students designed and built a prototype to make their own buzz wire game. Students then changed the materials used to make a more efficient version of the game

As a class, students sketched, designed and created a team mascot using the laser cutter

At In-School 2, students made an arrow diagram or collage

Description of research site and environment

Allina , B. ( 2018 ), “ The development of STEAM educational policy to promote student creativity and social empowerment ”, Arts Education Policy Review , Vol. 119 No. 2 , pp. 77 - 87 .

Amory , A. ( 2014 ), “ Tool-mediated authentic learning in an educational technology course: a designed-based innovation ”, Interactive Learning Environments , Vol. 22 No. 4 , pp. 497 - 513 .

Arthur , J. , Waring , M. , Coe , R. and Hedges , L.V. ( 2012 ), Research Methods and Methodologies in Education , Sage , Los Angeles, CA .

Blikstein , P. ( 2013 ), “ Digital fabrication and ‘making’ in education: the democratization of invention ”, FabLabs: Of Machines, Makers and Inventors , Vol. 4 , pp. 1 - 21 .

Conley , M. , Douglass , L. and Trinkley , R. ( 2014 ), “ Using inquiry principles of art to explore mathematical practice standards ”, Middle Grades Research Journal , Vol. 9 No. 3 , pp. 89 - 101 .

Costantino , T. ( 2018 ), “ Steam by another name: transdisciplinary practice in art and design education ”, Arts Education Policy Review , Vol. 119 No. 2 , pp. 100 - 106 .

Cousin , G. ( 2005 ), “ Case study research ”, Journal of Geography in Higher Education , Vol. 29 No. 3 , pp. 421 - 427 .

Gadanidis , G. ( 2015 ). “ Young children, mathematics, and coding: a low floor, high ceiling, wide walls environment ”, Cases on Technology Integration in Mathematics Education , IGI Global , Hershey, PA , pp. 308 - 329 .

Gadanidis , G. , Hughes , J. and Cordy , M. ( 2011 ), “ Mathematics for gifted students in an arts-and technology-rich setting ”, Journal for the Education of the Gifted , Vol. 34 No. 3 , pp. 397 - 433 .

Gall , M.D. , Gall , J.P. and Borg , W.R. ( 2007 ), Educational Research: An Introduction , Pearson Education , Boston .

Gess , A.H. ( 2017 ), “ STEAM education: separating fact from fiction ”, Technology and Engineering Teacher , Vol. 77 No. 3 , pp. 39 - 41 .

Halverson , E.R. and Sheridan , K.M. ( 2014 ), “ The maker movement in education ”, Harvard Educational Review , Vol. 84 No. 4 , pp. 495 - 504 .

Hughes , J.M. ( 2017 ), “ Digital making with ‘At-Risk’ youth ”, The International Journal of Information and Learning Technology , Vol. 34 No. 2 , pp. 102 - 113 .

Kafai , Y. , Proctor , C. and Lui , D. ( 2019 ), “ From theory bias to theory dialogue: embracing cognitive, situated, and critical framings of computational thinking in K-12 CS education ”, International Computing Education Research Conference (ICER '19) , August 12–14, 2019 , ACM , Toronto, ON, Canada, New York, NY, USA , p. 9 , doi: 10.1145/3291279.3339400 .

Kolb , A. and Kolb , D.A. ( 2005 ), Experiential Learning Theory Bibliography , Experience Based Learning Systems , Cleveland, OH .

Liao , C. ( 2016 ). “ From interdisciplinary to transdisciplinary: an arts-integrated approach to STEAM education ”, Art Education , Vol. 69 No. 6 , pp. 44 - 49 .

Liao , C. , Motter , J.L. and Patton , R.M. ( 2016 ), “ Tech-savvy girls: learning 21st-century skills through STEAM digital artmaking ”, Art Education , Vol. 69 No. 4 , pp. 29 - 35 .

Papert , S. ( 1980 ), Mindstorms: Children, Computers, and Powerful Ideas , Basic Books , New York, NY .

Reeves , T.C. , Herrington , J. and Oliver , R. ( 2004 ), “ A development research agenda for online collaborative learning ”, Educational Technology Research and Development , Vol. 52 , pp. 53 - 65 .

Stake , R. ( 2005 ), “ Qualitative case studies ”, in Denzin , N.K. and Licoln , Y.S. (Eds), The Sage Handbook of Qualitative Research , 3rd ed. , Sage , London , pp. 443 - 466 .

Yin , R.K. ( 2004 ), Case Study Methods , AERA , or Yin, R.K. (2009). Chapter 2. Case Study Research: Design and Methods, London, Sage, available at: http://www.cosmoscorp.com/Docs/AERAdraft.pdf .

Acknowledgements

The research assistantship for this article was supported by Western University and SSHRC.

Corresponding author

About the authors.

Marja G. Bertrand is a MA graduate from Western University and a teacher in Mathematics, Science, Biology, Chemistry and Physics. Presently, she is teaching for the local school board Grade 9, 10 and 11 Mathematics and working as a Senior Research Assistant at Western University. She is passionate about teaching and learning. She has presented at several conferences, seminars and workshops on STEM/STEAM education in Canada and abroad. She has also received several graduate awards from the Faculty of Education for her research on STEM/STEAM education. Specifically, the Art Geddis Memorial Award for her use of reflective practice as a critical lens to analyze the mathematics and science learning in the curriculum and pedagogy of the STEAM programs. She was also awarded the Joan Pedersen Memorial Graduate Award for her contribution to the “Early Years” education research. Her research interests are in STEM/STEAM education, Makerspaces, Designed-Based Learning and Computational Thinking tools.

Immaculate K. Namukasa is an Associate Professor of the Faculty of education and distinguished teaching fellow with the Center for Teaching and Learning from 2017 to 2020 at Western University in Ontario, Canada. She joined the Faculty of Education at Western from the University of Alberta, where she completed her Doctoral work in the department of Secondary Education. She is a past journal editor for the Ontario Mathematics Gazette – a magazine for teachers and educators and a current editor of the Math + code 'Zine. Namukasa collaborates with teachers in four public school boards, in one private school system, and with researchers and teachers in Canada, China, Thailand and Africa. Namukasa's current research interests lie in mathematics teacher education and professional development, integration of technology and computational thinking in mathematics education, mathematics learning tools, resources and activities, and curriculum and pedagogical reforms.

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Teaching Research Skills to K-12 Students in The Classroom

Research is at the core of knowledge. Nobody is born with an innate understanding of quantum physics. But through research , the knowledge can be obtained over time. That’s why teaching research skills to your students is crucial, especially during their early years.

But teaching research skills to students isn’t an easy task. Like a sport, it must be practiced in order to acquire the technique. Using these strategies, you can help your students develop safe and practical research skills to master the craft.

What Is Research?

By definition, it’s a systematic process that involves searching, collecting, and evaluating information to answer a question. Though the term is often associated with a formal method, research is also used informally in everyday life!

Whether you’re using it to write a thesis paper or to make a decision, all research follows a similar pattern.

• Choose a topic : Think about general topics of interest. Do some preliminary research to make sure there’s enough information available for you to work with and to explore subtopics within your subject.
• Develop a research question : Give your research a purpose; what are you hoping to solve or find?
• Collect data : Find sources related to your topic that will help answer your research questions. 
• Evaluate your data : Dissect the sources you found. Determine if they’re credible and which are most relevant.
• Make your conclusion : Use your research to answer your question! 

Why Do We Need It?

Research helps us solve problems. Trying to answer a theoretical question? Research. Looking to buy a new car? Research. Curious about trending fashion items? Research! 

Sometimes it’s a conscious decision, like when writing an academic paper for school. Other times, we use research without even realizing it. If you’re trying to find a new place to eat in the area, your quick Google search of “food places near me” is research!

Whether you realize it or not, we use research multiple times a day, making it one of the most valuable lifelong skills to have. And it’s why — as educators —we should be teaching children research skills in their most primal years. 

Teaching Research Skills to Elementary Students

In elementary school, children are just beginning their academic journeys. They are learning the essentials: reading, writing, and comprehension. But even before they have fully grasped these concepts, you can start framing their minds to practice research.

According to curriculum writer and former elementary school teacher, Amy Lemons , attention to detail is an essential component of research. Doing puzzles, matching games, and other memory exercises can help equip students with this quality before they can read or write. 

Improving their attention to detail helps prepare them for the meticulous nature of research. Then, as their reading abilities develop, teachers can implement reading comprehension activities in their lesson plans to introduce other elements of research. 

One of the best strategies for teaching research skills to elementary students is practicing reading comprehension . It forces them to interact with the text; if they come across a question they can’t answer, they’ll need to go back into the text to find the information they need. 

Some activities could include completing compare/contrast charts, identifying facts or questioning the text, doing background research, and setting reading goals. Here are some ways you can use each activity:

• How it translates : Step 3, collect data; Step 4, evaluate your data
• Questioning the text : If students are unsure which are facts/not facts, encourage them to go back into the text to find their answers. 
• How it translates : Step 3, collect data; Step 4, evaluate your data; Step 5, make your conclusion
• How it translates : Step 1, choose your topic
• How it translates : Step 2, develop a research question; Step 5, make your conclusion

Resources for Elementary Research

If you have access to laptops or tablets in the classroom, there are some free tools available through Pennsylvania’s POWER Kids to help with reading comprehension. Scholastic’s BookFlix and TrueFlix are 2 helpful resources that prompt readers with questions before, after, and while they read. 

• BookFlix : A resource for students who are still new to reading. Students will follow along as a book is read aloud. As they listen or read, they will be prodded to answer questions and play interactive games to test and strengthen their understanding. 

• TrueFlix : A resource for students who are proficient in reading. In TrueFlix, students explore nonfiction topics. It’s less interactive than BookFlix because it doesn’t prompt the reader with games or questions as they read. (There are still options to watch a video or listen to the text if needed!)

Teaching Research Skills to Middle School Students

By middle school, the concept of research should be familiar to students. The focus during this stage should be on credibility . As students begin to conduct research on their own, it’s important that they know how to determine if a source is trustworthy.

Before the internet, encyclopedias were the main tool that people used for research. Now, the internet is our first (and sometimes only) way of looking information up. 

Unlike encyclopedias which can be trusted, students must be wary of pulling information offline. The internet is flooded with unreliable and deceptive information. If they aren’t careful, they could end up using a source that has inaccurate information!

How To Know If A Source Is Credible

In general, credible sources are going to come from online encyclopedias, academic journals, industry journals, and/or an academic database. If you come across an article that isn’t from one of those options, there are details that you can look for to determine if it can be trusted.

• The author: Is the author an expert in their field? Do they write for a respected publication? If the answer is no, it may be good to explore other sources.
• Citations: Does the article list its sources? Are the sources from other credible sites like encyclopedias, databases, or journals? No list of sources (or credible links) within the text is usually a red flag. 
• Date: When was the article published? Is the information fresh or out-of-date? It depends on your topic, but a good rule of thumb is to look for sources that were published no later than 7-10 years ago. (The earlier the better!)
• Bias: Is the author objective? If a source is biased, it loses credibility.

An easy way to remember what to look for is to utilize the CRAAP test . It stands for C urrency (date), R elevance (bias), A uthority (author), A ccuracy (citations), and P urpose (bias). They’re noted differently, but each word in this acronym is one of the details noted above. 

If your students can remember the CRAAP test, they will be able to determine if they’ve found a good source.

Resources for Middle School Research

To help middle school researchers find reliable sources, the database Gale is a good starting point. It has many components, each accessible on POWER Library’s site. Gale Litfinder , Gale E-books , or Gale Middle School are just a few of the many resources within Gale for middle school students.

Teaching Research Skills To High Schoolers

The goal is that research becomes intuitive as students enter high school. With so much exposure and practice over the years, the hope is that they will feel comfortable using it in a formal, academic setting. 

In that case, the emphasis should be on expanding methodology and citing correctly; other facets of a thesis paper that students will have to use in college. Common examples are annotated bibliographies, literature reviews, and works cited/reference pages.

• Annotated bibliography : This is a sheet that lists the sources that were used to conduct research. To qualify as annotated , each source must be accompanied by a short summary or evaluation. 
• Literature review : A literature review takes the sources from the annotated bibliography and synthesizes the information in writing.
• Works cited/reference pages : The page at the end of a research paper that lists the sources that were directly cited or referenced within the paper. 

Resources for High School Research

Many of the Gale resources listed for middle school research can also be used for high school research. The main difference is that there is a resource specific to older students: Gale High School . 

If you’re looking for some more resources to aid in the research process, POWER Library’s e-resources page allows you to browse by grade level and subject. Take a look at our previous blog post to see which additional databases we recommend.

Visit POWER Library’s list of e-resources to start your research!

• Our Mission

Getting First Graders Started With Research

Teaching academically honest research skills helps first graders learn how to collect, organize, and interpret information.

Earlier in my career, I was told two facts that I thought to be false: First graders can’t do research, because they aren’t old enough; and if facts are needed for a nonfiction text, the students can just make them up. Teachers I knew went along with this misinformation, as it seemed to make teaching and learning easier. I always felt differently, and now—having returned to teaching first grade 14 years after beginning my career with that age group—I wanted to prove that first graders can and should learn how to research. 

A lot has changed over the years. Not only has the science of reading given teachers a much better understanding of how to teach reading skills , but we now exist in a culture abundant in information and misinformation. It’s imperative that we teach academically honest research skills to students as early as possible. 

Use a Familiar Resource, and Pair it with a Planned Unit

How soon do you start research in first grade? Certainly not at the start of the year with the summer lapse in skills and knowledge and when new students aren’t yet able to read. By December of this school year, skills had either been recovered or established sufficiently that I thought we could launch into research. This also purposely coincided with a unit of writing on nonfiction—the perfect pairing.

The research needed an age-related focus to make it manageable, so I chose animals. I thought about taking an even safer route and have one whole class topic that we researched together, so that students could compare notes and skills. I referred back to my days working in inquiry-based curriculums (like the International Baccalaureate Primary Years Program) and had students choose which animal to study. Our school librarian recommended that we use Epic because the service has an abundance of excellent nonfiction animal texts of different levels.

Teach the Basics for Organized Research 

I began with a conversation about academic honesty and why we don’t just copy information from books. We can’t say this is our knowledge if we do this; it belongs to the author. Instead, we read and learn. Then, we state what we learned in our own words. Once this concept is understood, I model how to do this by creating a basic step-by-step flowchart taught to me by my wife—a longtime first-grade and kindergarten teacher and firm believer in research skills.

• Read one sentence at a time.
• Turn the book over or the iPad around.
• Think about what you have learned. Can you remember the fact? Is the fact useful? Is it even a fact?
• If the answer is no, reread the sentence or move onto the next one.
• If the answer is yes, write the fact in your own words. Don’t worry about spelling. There are new, complex vocabulary words, so use your sounding-out/stretching-out strategies just like you would any other word. Write a whole sentence on a sticky note.
• Place the sticky note in your graphic organizer. Think about which section it goes in. If you aren’t sure, place it in the “other facts” section.

The key to collecting notes is the challenging skill of categorizing them. I created a graphic organizer that reflected the length and sections of the exemplar nonfiction text from our assessment materials for the writing unit. This meant it had five pages: an introduction, “what” the animal looks like, “where” the animal lives, “how” the animal behaved, and a last page for “other facts” that could become a general conclusion.

Our district’s literacy expert advised me not to hand out my premade graphic organizer too soon in this process because writing notes and categorizing are two different skills. This was my intention, but I forgot the good advice and handed out the organizer right away. This meant dedicating time for examining and organizing notes in each combined writing and reading lesson. A lot of one-on-one feedback was needed for some students, while others flourished and could do this work independently. The result was that the research had a built-in extension for those students who were already confident readers.

Focus on What Students Need to Practice 

Research is an essential academic skill but one that needs to be tackled gradually. I insisted that my students use whole sentences rather than words or phrases because they’re at the stage of understanding what a complete sentence is and need regular practice. In this work, there’s no mention of citation language and vetting sources; in the past, I’ve introduced those concepts to students in fourth grade and used them regularly with my fifth-grade students. Finding texts that span the reading skill range of a first-grade class is a big enough task. 

For some of the key shared scientific vocabulary around science concepts, such as animal groups (mammals, etc.) or eating habits (carnivore, etc.), I created class word lists, having first sounded out the words with the class and then asked students to attempt spelling them in their writing.

The Power of Research Can Facilitate Student Growth 

I was delighted with the results of the research project. In one and a half weeks, every student had a graphic organizer with relevant notes, and many students had numerous notes. With my fourth- and fifth-grade students, I noticed that one of the biggest difficulties for them was taking notes and writing them in a way that showed a logical sequence. Therefore, we concluded our research by numbering the notes in each section to create a sequential order. 

This activity took three lessons and also worked for my first graders. These organized notes created an internal structure that made the next step in the writing process, creating a first draft of their nonfiction teaching books, so much easier. 

The overall result was that first graders were able to truly grasp the power of research and gathering accurate facts. I proved that young children can do this, especially when they work with topics that already fascinate them. Their love of learning motivated them to read higher-level and more sophisticated texts than they or I would normally pick, further proving how interest motivates readers to embrace complexity.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 09 May 2024

Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors

• Yujie Su   ORCID: orcid.org/0000-0003-1444-1598 1 ,
• Xiaoshu Xu   ORCID: orcid.org/0000-0002-0667-4511 1 ,
• Yunfeng Zhang 2 ,
• Xinyu Xu 1 &
• Shanshan Hao 3  

Humanities and Social Sciences Communications volume  11 , Article number:  594 ( 2024 ) Cite this article

112 Accesses

Metrics details

• Language and linguistics

This study delves into the retrospections of undergraduate students concerning their online learning experiences after the COVID-19 pandemic, using the nine key influencing factors: behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, and self-regulation. 46 Year 1 students from a comprehensive university in China were asked to maintain reflective diaries throughout an academic semester, providing first-person perspectives on the strengths and weaknesses of online learning. Meanwhile, 18 college teachers were interviewed with the same questions as the students. Using thematic analysis, the research identified 9 factors. The research revealed that instruction ranked highest among the 9 factors, followed by engagement, self-regulation, interaction, motivation, and others. Moreover, teachers and students had different attitudes toward instruction. Thirdly, teacher participants were different from student participants given self-efficacy and self-regulation due to their variant roles in online instruction. Lastly, the study reflected students were not independent learners, which explained why instruction ranked highest in their point of view. Findings offer valuable insights for educators, administrators, and policy-makers involved in higher education. Recommendations for future research include incorporating a more diverse sample, exploring relationships between the nine factors, and focusing on equipping students with skills for optimal online learning experiences.

Similar content being viewed by others

Sleep quality, duration, and consistency are associated with better academic performance in college students

Interviews in the social sciences

A systematic review and multivariate meta-analysis of the physical and mental health benefits of touch interventions

Introduction.

The outbreak of the COVID-19 pandemic has had a profound impact on education worldwide, leading to the widescale adoption of online learning. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), at the peak of the pandemic, 192 countries had implemented nationwide closures, affecting approximately 99% of the world’s student population (UNESCO 2020 a). In response, educational institutions, teachers, and students quickly adapted to online learning platforms, leveraging digital technologies to continue education amidst the crisis (Marinoni et al. 2020 ).

The rapid and unexpected shift to online learning brought about a surge in research aiming to understand its impact, effectiveness, and challenges. Researchers across the globe have been investigating various dimensions of online learning. Some focus on students’ experiences and perspectives (Aristovnik et al. 2021 ), technological aspects (Bao 2020 ), pedagogical strategies (Hodges et al. 2020 ), and the socio-emotional aspect of learning (Ali 2020 ). Tan et al. ( 2021 ) found that motivation and satisfaction were mostly positively perceived by students, and lack of interaction was perceived as an unfavorable online instruction perception. Some center on teachers’ perceptions of the benefits and challenges (Lucas and Vicente, 2023 ; Mulla et al. 2023 ), post-pandemic pedagogisation (Rapanta et al. 2021 ), and post-pandemic further education (Kohnke et al. 2023 ; Torsani et al. 2023 ). It was worth noting that elements like interaction and engagement were central to the development and maintenance of the learning community (Lucas and Vincente 2023 ),

The rise of online learning has also posed unprecedented challenges. Studies have pointed out the digital divide and accessibility issues (Crawford et al. 2020 ), students’ motivation and engagement concerns (Martin and Bolliger 2018 ), and the need for effective online instructional practices (Trust and Whalen 2020 ). The rapid transition to online learning has highlighted the need for robust research to address these challenges and understand the effectiveness of online learning in this new educational paradigm.

Despite the extensive research on online learning during and after the COVID-19 pandemic, there remains a notable gap in understanding the retrospective perspectives of both undergraduates and teachers. Much of the current literature has focused on immediate response strategies to the transition to online learning, often overlooking the detailed insights that reflective retrospection can provide (Marinoni et al. 2020 ; Bao 2020 ). In addition, while many studies have examined isolated aspects of online learning, they have not often employed a comprehensive framework, leaving undergraduates’ voices, in particular, underrepresented in the discourse (Aristovnik et al. 2021 ; Crawford et al. 2020 ). This study, situated in the context of the COVID-19 pandemic’s impetus toward online learning, seeks to fill this crucial gap. By exploring online learning from the perspectives of both instructors and undergraduates, and analyzing nine key factors that include engagement, motivation, and self-efficacy, the research contributes vital insights into the dynamics of online education (Wang and Wang 2021 ). This exploration is especially pertinent as digital learning environments become increasingly prevalent worldwide (UNESCO 2020b ). The findings of our study are pivotal for shaping future educational policies and enhancing online education strategies in this continuously evolving educational landscape (Greenhow et al. 2021 ). Thus, three research questions were raised:

Q1: How do undergraduates and teachers in China retrospectively perceive the effectiveness of online learning after the COVID-19 pandemic?

Q2: Which of the nine outcome influencing factors had the most significant impact on online learning experiences after the pandemic, and why?

Q3: What recommendations can be proposed to enhance the effectiveness of online learning in the future?

The research takes place at a comprehensive university in China, with a sample of 46 Year 1 students and 18 experienced teachers. Their reflections on the effectiveness of online learning were captured through reflective diaries guided by four questions. These questions investigated the students’ online learning states and attitudes, identified issues and insufficiencies in online learning, analyzed the reasons behind these problems, and proposed improvements. By assessing their experiences and perceptions, we seek to explore the significant factors that shaped online learning outcomes after the pandemic and the means to enhance its effectiveness.

This paper first presents a review of the existing literature, focusing on the impact of the pandemic on online learning and discussing the nine significant factors influencing online learning outcomes. Following this, the methodology utilized for this study is detailed, setting the stage for a deeper understanding of the research process. Subsequently, we delve into the results of the thematic analysis conducted based on undergraduate students and teachers’ retrospections. Finally, the paper concludes by offering meaningful implications of the findings for various stakeholders and suggesting directions for future research in this critical area.

Literature review

Online learning application and evaluation in higher education.

Online learning, also known as e-learning or distance learning, refers to education that takes place over the Internet rather than in a traditional classroom setting. It has seen substantial growth over the past decade and has been accelerated due to the COVID-19 pandemic (Trust and Whalen 2020 ). Online learning allows for a flexible learning environment, breaking the temporal and spatial boundaries of traditional classroom settings (Bozkurt and Sharma 2020 ). In response to the COVID-19 pandemic, educational institutions globally have embraced online learning at an unprecedented scale. This has led to an immense surge in research focusing on the effects of the pandemic on online learning (Crawford et al. 2020 ; Marinoni et al. 2020 ).

Researchers were divided in their attitudes toward the effects of online learning, including positive, neutral, and negative. Research by Bahasoan et al. ( 2020 ), Bernard et al. ( 2004 ), Hernández-Lara and Serradell-López ( 2018 ), and Paechter and Maier ( 2010 ) indicated the effectiveness of online learning, including improved outcomes and engagement in online formats, providing flexibility and enhancing digital skills for instance. Research, including studies by Dolan Hancock and Wareing ( 2015 ) and Means et al. ( 2010 ), indicates that under equivalent conditions and with similar levels of support, there is frequently no substantial difference in learning outcomes between traditional face-to-face courses and completely online courses.

However, online learning was not without its challenges. Research showing less favorable results for specific student groups can be referenced in Dennen ( 2014 ), etc. The common problems faced by students included underdeveloped independent learning ability, lack of motivation, difficulties in self-regulation, student engagement and technical issues (Aristovnik et al. 2021 ; Martin and Bolliger 2018 ; Song et al. 2004 ; Zheng et al. 2022 ).

Moreover, factors like instructional strategies, course design, etc. were also linked to learning outcomes and successful online learning (Ali 2020 ; Hongsuchon et al. 2022 ). Careaga-Butter et al. ( 2020 ) critically analyze online education in pandemic and post-pandemic contexts, focusing on digital tools and resources for teaching in synchronous and asynchronous learning modalities. They discuss the swift adaptation to online learning during the pandemic, highlighting the importance of technological infrastructure, pedagogical strategies, and the challenges of digital divides. The article emphasizes the need for effective online learning environments and explores trends in post-pandemic education, providing insights into future educational strategies and practices.

Determinants of online learning outcomes

Online learning outcomes in this paper refer to the measurable educational results achieved through online learning methods, including knowledge acquisition, skill development, changes in attitudes or behaviors, and performance improvements (Chang 2016 ; Panigrahi et al. 2018 ). The literature review identified key factors influencing online learning outcomes, emphasizing their significant role in academic discourse. These factors, highlighted in scholarly literature, include student engagement, instructional design, technology infrastructure, student-teacher interaction, and student self-regulation.

Student Engagement: The level of a student’s engagement significantly impacts their learning outcomes. The more actively a student is engaged with the course content and activities, the better their performance tends to be. This underscores the importance of designing engaging course content and providing opportunities for active learning in an online environment (Martin and Bolliger 2018 ).

Instructional Design: How an online course is designed can greatly affect student outcomes. Key elements such as clarity of learning objectives, organization of course materials, and the use of diverse instructional strategies significantly impact student learning (Bozkurt and Sharma 2020 ).

Technology Infrastructure: The reliability and ease of use of the learning management system (LMS) also play a significant role in online learning outcomes. When students experience technical difficulties, it can lead to frustration, reduced engagement, and lower performance (Johnson et al. 2020 ).

Student-Teacher Interaction: Interaction between students and teachers in an online learning environment is a key determinant of successful outcomes. Regular, substantive feedback from instructors can promote student learning and motivation (Boling et al. 2012 ).

Student Self-Regulation: The autonomous nature of online learning requires students to be proficient in self-regulated learning, which involves setting learning goals, self-monitoring, and self-evaluation. Students who exhibit strong self-regulation skills are more likely to succeed in online learning (Broadbent 2017 ).

While many studies have investigated individual factors affecting online learning, there is a paucity of research offering a holistic view of these factors and their interrelationships, leading to a fragmented understanding of the influences on online learning outcomes. Given the multitude of experiences and variables encompassed by online learning, a comprehensive framework like is instrumental in ensuring a thorough investigation and interpretation of the breadth of students’ experiences.

Students’ perceptions of online learning

Understanding students’ perceptions of online learning is essential for enhancing its effectiveness and student satisfaction. Studies show students appreciate online learning for its flexibility and convenience, offering personalized learning paths and resource access (Händel et al. 2020 ; Johnson et al. 2020 ). Yet, challenges persist, notably in maintaining motivation and handling technical issues (Aristovnik et al. 2021 ; Händel et al. 2020 ). Aguilera-Hermida ( 2020 ) reported mixed feelings among students during the COVID-19 pandemic, including feelings of isolation and difficulty adjusting to online environments. Boling et al. ( 2012 ) emphasized students’ preferences for interactive and communicative online learning environments. Additionally, research indicates that students seek more engaging content and innovative teaching approaches, suggesting a gap between current online offerings and student expectations (Chakraborty and Muyia Nafukho 2014 ). Students also emphasize the importance of community and peer support in online settings, underlining the need for collaborative and social learning opportunities (Lai et al. 2019 ). These findings imply that while online learning offers significant benefits, addressing its shortcomings is critical for maximizing its potential.

The pandemic prompted a reconsideration of instructional modalities, with many students favoring face-to-face instruction due to the immediacy and focus issues (Aristovnik et al. 2021 ; Trust and Whalen 2020 ). Despite valuable insights, research gaps remain, particularly in long-term undergraduate reflections and the application of nine factors of comprehensive frameworks, indicating a need for more holistic research in online learning effectiveness.

Teachers’ perceptions of online learning

The pandemic has brought attention to how teachers manage instruction in virtual learning environments. Teachers and students are divided in terms of their attitudes toward online learning. Some teachers and students looked to the convenience and flexibility of online learning (Chuenyindee et al. 2022 ; Al-Emran and Shaalan 2021 ). They conceived that online learning provided opportunities to improve educational equality as well (Tenório et al. 2016 ). Even when COVID-19 was over, the dependence on online learning was likely here to stay, for some approaches of online learning were well-received by students and teachers (Al-Rahmi et al. 2019 ; Hongsuchon et al. 2022 ).

Teachers had shown great confidence in delivering instruction in an online environment in a satisfying manner. They also agreed that the difficulty of teaching was closely associated with course structures (Gavranović and Prodanović 2021 ).

Not all were optimistic about the effects of online learning. They sought out the challenges facing teachers and students during online learning.

A mixed-method study of K-12 teachers’ feelings, experiences, and perspectives that the major challenges faced by teachers during the COVID-19 pandemic were lack of student participation and engagement, technological support for online learning, lack of face-to-face interactions with students, no work-life balance and learning new technology.

The challenges to teachers’ online instruction included instruction technology (Maatuk et al. 2022 ; Rasheed et al. 2020 ), course design (Khojasteh et al. 2023 ), and teachers’ confidence (Gavranović and Prodanović 2021 ).

Self-regulation challenges and challenges in using technology were the key challenges to students, while the use of technology for teaching was the challenge facing teachers (Rasheed et al. 2020 ).

The quality of course design was another important factor in online learning. A research revealed the competency of the instructors and their expertise in content development contributed a lot to students’ satisfaction with the quality of e-contents.

Theoretical framework

The theoretical foundation of the research is deeply rooted in multifaceted framework for online learning, which provides a comprehensive and interwoven model encompassing nine critical factors that collectively shape the educational experience in online settings. This framework is instrumental in guiding our analysis and enhances the comparability and interpretability of our results within the context of existing literature.

Central to Yu’s framework is the concept of behavioral intention, which acts as a precursor to student engagement in online learning environments. This engagement, inherently linked to the students’ intentions and motivations, is significantly influenced by the quality of instruction they receive. Instruction, therefore, emerges as a pivotal element in this model, directly impacting not only student engagement but also fostering a sense of self-efficacy among learners. Such self-efficacy is crucial as it influences both the performance of students and their overall satisfaction with the learning process.

The framework posits that engagement, a derivative of both strong behavioral intention and effective instruction, plays a vital role in enhancing student performance. This engagement is tightly interlaced with self-regulation, an indispensable skill in the autonomous and often self-directed context of online learning. Interaction, encompassing various forms such as student-teacher and peer-to-peer communications, further enriches the learning experience. It significantly contributes to the development of motivation and self-efficacy, both of which are essential for sustaining engagement and fostering self-regulated learning.

Motivation, especially when intrinsically driven, acts as a catalyst, perpetuating engagement and self-regulation, which ultimately leads to increased satisfaction with the learning experience. In this framework, self-efficacy, nurtured through effective instruction and meaningful interactions, has a positive impact on students’ performance and satisfaction, thereby creating a reinforcing cycle of learning and achievement.

Performance in this model is viewed as a tangible measure of the synergistic interplay of engagement, instructional quality, and self-efficacy, while satisfaction reflects the culmination of the learning experience, shaped by the quality of instruction, the extent and nature of interactions, and the flexibility of the learning environment. This satisfaction, in turn, influences students’ future motivation and their continued engagement with online learning.

Yu’s model thus presents a dynamic ecosystem where changes in one factor can have ripple effects across the entire spectrum of online learning. It emphasizes the need for a holistic approach in the realm of online education, considering the complex interplay of these diverse yet interconnected elements to enhance both the effectiveness and the overall experience of online learning.

The current study employed a qualitative design to explore teachers’ and undergraduates’ retrospections on the effectiveness of online learning during the first semester of the 2022–2023 school year, which is in the post-pandemic period. Data were collected using reflective diaries, and thematic analysis was applied to understand the experiences based on the nine factors.

Sample and sampling

The study involved 18 teachers and 46 first-year students from a comprehensive university in China, selected through convenience sampling to ensure diverse representation across academic disciplines. To ensure a diverse range of experiences in online learning, the participant selection process involved an initial email inquiry about their prior engagement with online education. The first author of this study received ethics approval from the department research committee, and participants were informed of the study’s objectives two weeks before via email. Only those participants who provided written informed consent were included in the study and were free to withdraw at any time. Pseudonyms were used to protect participants’ identities during the data-coding process. For direct citations, acronyms of students’ names were used, while “T+number” was used for citations from teacher participants.

The 46 students are all first-year undergraduates, 9 females and 37 males majoring in English and non-English (see Table 1 ).

The 18 teachers are all experienced instructors with at least 5 years of teaching experience, 13 females and 5 male, majoring in English and Non-English (see Table 2 ).

Data collection

Students’ data were collected through reflective diaries in class during the first semester of the 2022–2023 school year. Each participant was asked to maintain a diary over the course of one academic semester, in which they responded to four questions.

The four questions include:

What was your state and attitude toward online learning?

What were the problems and shortcomings of online learning?

What do you think are the reasons for these problems?

What measures do you think should be taken to improve online learning?

This approach provided a first-person perspective on the participants’ online teaching or learning experiences, capturing the depth and complexity of their retrospections.

Teachers were interviewed separately by responding to the four questions the same as the students. Each interview was conducted in the office or the school canteen during the semester and lasted about 20 to 30 min.

Data analysis

We utilized thematic analysis to interpret the reflective diaries, guided initially by nine factors. This method involved extensive engagement with the data, from initial coding to the final report. While Yu’s factors provided a foundational structure, we remained attentive to new themes, ensuring a comprehensive analysis. Our approach was methodical: familiarizing ourselves with the data, identifying initial codes, systematically searching and reviewing themes, and then defining and naming them. To validate our findings, we incorporated peer debriefing, and member checking, and maintained an audit trail. This analysis method was chosen for its effectiveness in extracting in-depth insights from undergraduates’ retrospections on their online learning experiences post-pandemic, aligning with our research objectives.

According to the nine factors, the interviews of 18 teachers and 46 Year 1 undergraduates were catalogued and listed in Table 3 .

Behavioral intention towards online learning post-pandemic

Since the widespread of the COVID-19 pandemic, both teachers and students have experienced online learning. However, their online teaching or learning was forced rather than planned (Baber 2021 ; Bao 2020 ). Students more easily accepted online learning when they perceived the severity of COVID-19.

When entering the post-pandemic era, traditional teaching was resumed. Students often compared online learning with traditional learning by mentioning learning interests, eye contact, face-to-face learning and learning atmosphere.

“I don’t think online learning is a good form of learning because it is hard to focus on learning.” (DSY) “In unimportant courses, I would let the computer log to the platform and at the same time do other entertains such as watching movies, listening to the music, having snacks or do the cleaning.” (XYN) “Online learning makes it impossible to have eye contact between teachers and students and unable to create a face-to-face instructional environment, which greatly influences students’ initiative and engagement in classes.” (WRX)

They noted that positive attitudes toward online learning usually generated higher behavioral intention to use online learning than those with negative attitudes, as found in the research of Zhu et al. ( 2023 ). So they put more blame on distractions in the learning environment.

“Online learning relies on computers or cell phones which easily brings many distractions. … I can’t focus on studying, shifting constantly from study and games.” (YX) “When we talk about learning online, we are hit by an idea that we can take a rest in class. It’s because everyone believes that during online classes, the teacher is unable to see or know what we are doing.” (YM) “…I am easily disturbed by external factors, and I am not very active in class.” (WZB)

Teachers reported a majority of students reluctantly turning on their cameras during online instruction and concluded the possible reason for such behavior.

“One of the reasons why some students are unwilling to turn on the camera is that they are worried about their looks and clothing at home, or that they don’t want to become the focus.” (T4)

They also noticed students’ absent-mindedness and lazy attitude during online instruction.

“As for some students who are not self-regulated, they would not take online learning as seriously as offline learning. Whenever they are logged onto the online platform, they would be unable to stay focused and keep their attention.” (T1)

Challenges and opportunities in online instruction post-pandemic

Online teaching brought new challenges and opportunities for students during and after the pandemic. The distractions at home seemed to be significantly underestimated by teachers in an online learning environment (Radmer and Goodchild 2021 ). It might be the reason why students greatly expected and heavily relied on teachers’ supervision and management.

“The biggest problem of online learning is that online courses are as imperative as traditional classes, but not managed face to face the same as the traditional ones.” (PC) “It is unable to provide some necessary supervision.” (GJX) “It is incapable of giving timely attention to every student.” (GYC) “Teachers can’t understand students’ conditions in time in most cases so teachers can’t adjust their teaching plan.” (MZY) “Some courses are unable to reach the teaching objectives due to lack of experimental conduction and practical operation.” (YZH) “Insufficient teacher-student interaction and the use of cell phones make both groups unable to engage in classes. What’s more, though online learning doesn’t put a high requirement for places, its instructional environment may be crucial due to the possible distractions.” (YCY)

Teachers also viewed online instruction as an addition to face-to-face instruction.

“Online learning cannot run as smoothly as face-to-face instruction, but it can provide an in-time supplement to the practical teaching and students’ self-learning.” (T13, T17) “Online instruction is an essential way to ensure the normal function of school work during the special periods like the pandemic” (T1, T15)

Factors influencing student engagement in online learning

Learning engagement was found to contribute to gains in the study (Paul and Diana 2006 ). It was also referred to as a state closely intertwined with the three dimensions of learning, i.e., vigor, dedication, and absorption (Schaufeli et al. 2002 ). Previous studies have found that some key factors like learning interaction, self-regulation, and social presence could influence learning engagement and learning outcomes (Lowenthal and Dunlap 2020 ; Ng 2018 ). Due to the absence of face-to-face interaction like eye contact, facial expressions and body language, both groups of interviewees agreed that the students felt it hard to keep their attention and thus remain active in online classes.

“Students are unable to engage in study due to a lack of practical learning environment of online learning.” (ZMH, T12) “Online platforms may not provide the same level of engagement and interaction as in-person classrooms, making it harder for students to ask questions or engage in discussions.” (HCK) “The Internet is cold, lack of emotional clues and practical connections, which makes it unable to reproduce face-to-face offline learning so that teachers and students are unlikely to know each other’s true feelings or thoughts. In addition, different from the real-time learning supervision in offline learning, online learning leaves students more learning autonomy.” (XGH) “Lack of teachers’ supervision and practical learning environment, students are easily distracted.” (LMA, T9)

Just as Zhu et al. ( 2023 ) pointed out, we had been too optimistic about students’ engagement in online learning, because online learning relied more on students’ autonomy and efforts to complete online learning.

Challenges in teacher-student interaction in online learning

Online learning has a notable feature, i.e., a spatial and temporal separation among teachers and students. Thus, online teacher-student interactions, fundamentals of relationship formation, have more challenges for both teachers and students. The prior studies found that online interaction affected social presence and indirectly affected learning engagement through social presence (Miao and Ma 2022 ). In the present investigation, both teachers and students noted the striking disadvantage of online interaction.

“Online learning has many problems such as indirect teacher-student communication, inactive informative communication, late response of students and their inability to reflect their problems. For example, teachers cannot evaluate correctly whether the students have mastered or not.” (YYN) “Teachers and students are separated by screens. The students cannot make prompt responses to the teachers’ questions via loudspeakers or headphones. It is not convenient for students to participate in questioning and answering. …for most of the time, the students interact with teachers via typing.” (ZJY) “While learning online, students prefer texting the questions to answering them via the loudspeaker.”(T7)

Online learning interaction was also found closely related to online learning engagement, performance, and self-efficacy.

“Teachers and students are unable to have timely and effective communication, which reduces the learning atmosphere. Students are often distracted. While doing homework, the students are unable to give feedback to teachers.” (YR) “Students are liable to be distracted by many other side matters so that they can keep their attention to online learning.” (T15)

In the online learning environment, teachers need to make efforts to build rapport and personalizing interactions with students to help them perform better and achieve greater academic success (Harper 2018 ; Ong and Quek 2023 ) Meanwhile, teachers should also motivate students’ learning by designing the lessons, giving lectures and managing the processes of student interactions (Garrison 2003 ; Ong and Quek 2023 ).

Determinants of self-efficacy in online learning

Online learning self-efficacy refers to students’ perception of their abilities to fulfill specific tasks required in online learning (Calaguas and Consunji 2022 ; Zimmerman and Kulikowich 2016 ). Online learning self-efficacy was found to be influenced by various factors including task, learner, course, and technology level, among which task level was found to be most closely related (Xu et al. 2022 ). The responses from the 46 student participants reveal a shared concern, albeit without mentioning specific tasks; they highlight critical aspects influencing online learning: learner attributes, course structure, and technological infrastructure.

One unifying theme from the student feedback is the challenge of self-regulation and environmental distractions impacting learning efficacy. For instance, participant WSX notes the necessity for students to enhance time management skills due to deficiencies in self-regulation, which is crucial for successful online learning. Participant WY expands on this by pointing out the distractions outside traditional classroom settings, coupled with limited teacher-student interaction, which hampers idea exchange and independent thought, thereby undermining educational outcomes. These insights suggest a need for strategies that bolster students’ self-discipline and interactive opportunities in virtual learning environments.

On the technological front, participants WT and YCY address different but related issues. Participant WT emphasizes the importance of up-to-date course content and learning facilities, indicating that outdated materials and tools can significantly diminish the effectiveness of online education. Participant YCY adds to this by highlighting problems with online learning applications, such as subpar functionalities that can introduce additional barriers to learning.

Teacher participants, on the other hand, shed light on objective factors predominantly related to course content and technology. Participant T5’s response underscores the heavy dependency on technological advancement in online education and points out the current inability of platforms or apps to adequately monitor student engagement and progress. Participant T9 voices concerns about course content not being updated or aligned with contemporary trends and student interests, suggesting a disconnect between educational offerings and learner needs. Meanwhile, participant T8 identifies unstable network services as a significant hindrance to online teaching, highlighting infrastructure as a critical component of online education’s success.

Teachers also believed the insufficient mastery of facilities and unfamiliarity with online instruction posed difficulty.

“Most teachers and students are not familiar with online instruction. For example, some teachers are unable to manage online courses so they cannot design the courses well. Some students lack self-regulation, which leads to their distraction or avoidance in class.” (T9)

Influences on student performance in online learning

Students’ performance during online lessons is closely associated with their satisfaction and self-efficacy. Most of the student participants reflected on their distractions, confusion, and needs, which indicates their dissatisfaction with online learning.

“During online instruction, it is convenient for the students to make use of cell phones, but instead, cell phones bring lots of distraction.” (YSC) “Due to the limits of online learning, teachers are facing the computer screen and unable to know timely students’ needs and confusion. Meanwhile, it’s inconvenient for teachers to make clear explanations of the sample questions or problems.” (HZW)

They thought their low learning efficiency in performance was caused by external factors like the learning environment.

“The most obvious disadvantage of online learning goes to low efficiency. Students find it hard to keep attention to study outside the practical classroom or in a relaxing environment.” (WY) “Teachers are not strict enough with students, which leads to ineffective learning.” (WRX)

Teacher participants conceived students’ performance as closely related to valid online supervision and students’ self-regulation.

“Online instruction is unable to create a learning environment, which helps teachers know students’ instant reaction. Only when students well regulate themselves and stay focused during online learning can they achieve successful interactions and make good accomplishments in the class.” (T11) “Some students need teachers’ supervision and high self-regulation, or they were easily distracted.” (T16)

Student satisfaction and teaching effectiveness in online learning

Online learning satisfaction was found to be significantly and positively associated with online learning self-efficacy (Al-Nasa’h et al. 2021 ; Lashley et al. 2022 ). Around 46% of student participants were unsatisfied with teachers’ ways of teaching.

“Comparatively, bloggers are more interesting than teachers’ boring and dull voices in online learning.” (DSY) “Teachers’ voice sounds dull and boring through the internet, which may cause listeners to feel sleepy, and the teaching content is not interesting enough to the students.” (MFE)

It reflected partly that some teachers were not adapted to online teaching possibly due to a lack in experience of online teaching or learning (Zhu et al. 2022 ).

“Some teachers are not well-prepared for online learning. They are particularly unready for emergent technological problems when delivering the teaching.” (T1) “One of the critical reasons lies in the fact that teachers and students are not well trained before online learning. In addition, the online platform is not unified by the college administration, which has led to chaos and difficulty of online instruction.” (T17)

Teachers recognized their inadequate preparation and mastery of online learning as one of the reasons for dissatisfaction, but student participants exaggerated the role of teachers in online learning and ignored their responsibility in planning and managing their learning behavior, as in the research of (Xu et al. 2022 ).

The role of self-regulation in online learning success

In the context of online learning, self-regulation stands out as a crucial factor, necessitating heightened levels of student self-discipline and autonomy. This aspect, as Zhu et al. ( 2023 ) suggest, grants students significant control over their learning processes, making it a vital component for successful online education.

“Online learning requires learners to be of high discipline and self-regulation. Without good self-regulation, they are less likely to be effective in online learning.” (YZJ) “Most students lack self-control, unable to control the time of using electronic products. Some even use other electronic products during online learning, which greatly reduces their efficiency in learning.” (GPY) “Students are not well developed in self-control and easily distracted. Thus they are unable to engage fully in their study, which makes them unable to keep up with others” (XYN)

Both groups of participants had a clear idea of the positive role of self-regulation in successful learning, but they also admitted that students need to strengthen their self-regulation skills and it seemed they associated with the learning environment, learning efficiency and teachers’ supervision.

“If they are self-motivated, online learning can be conducted more easily and more efficiently. However, a majority are not strong in regulating themselves. Teachers’ direct supervision in offline learning can do better in motivating them to study hard…lack of interaction makes students less active and motivated.” (LY) “Students have a low level of self-discipline. Without teachers’ supervision, they find it hard to listen attentively or even quit listening. Moreover, in class, the students seldom think actively and independently.” (T13)

The analysis of participant responses, categorized into three distinct attitude groups – positive, neutral, and negative – reveals a multifaceted view of the disadvantages of online learning, as shown in Tables 4 and 5 . This classification provides a clearer understanding of how attitudes towards online learning influence perceptions of self-regulation and other related factors.

In Table 4 , the division among students is most pronounced in terms of interaction and self-efficacy. Those with neutral attitudes highlighted interaction as a primary concern, suggesting that it is less effective in an online setting. Participants with positive attitudes noted a lack of student motivation, while those with negative views emphasized the need for better self-efficacy. Across all attitudes, instruction, engagement, self-regulation, and behavior intention were consistently identified as areas needing improvement.

Table 5 sheds light on teachers’ perspectives, revealing a consensus on the significance and challenges of instruction, motivation, and self-efficacy in online learning. Teachers’ opinions vary most significantly on self-efficacy and engagement. Those with negative attitudes point to self-efficacy and instructional quality as critical areas needing attention, while neutral attitudes focus on the role of motivation.

Discussions

Using a qualitative and quantitative analysis of the questionnaire data showed that among the 18 college teachers and 46 year 1 undergraduate students of various majors taking part in the interview, about 38.9% of teachers and about 30.4% of students supported online learning. Only two teachers were neutral about online learning, and 50% of teachers did not support virtual learning. The percentages of students who expressed positive and neutral views on online learning were the same, i.e., 34.8%. This indicates that online learning could serve as a complementary approach to traditional education, yet it is not without challenges, particularly in terms of student engagement, self-regulation, and behavioral intention, which were often attributed to distractions inherent in online environments.

In analyzing nine factors, it was evident that both teachers and students did not perceive these factors uniformly. Instruction was a significant element for both groups, as validated by findings in Tables 3 and 5 . The absence of face-to-face interactions in online learning shifted the focus to online instruction quality. Teachers cited technological challenges as a central concern, while students criticized the lack of engaging content and teaching methods. This aligns with Miao and Ma ( 2022 ), who argued that direct online interaction does not necessarily influence learner engagement, thus underscoring the need for integrated approaches encompassing interactions, self-regulation, and social presence.

Furthermore, the role of technology acceptance in shaping self-efficacy was highlighted by Xu et al. ( 2022 ), suggesting that students with higher self-efficacy tend to challenge themselves more. Chen and Hsu ( 2022 ) noted the positive influence of using emojis in online lessons, emphasizing the importance of innovative pedagogical approaches in online settings.

The study revealed distinct priorities between teachers and students in online learning: teachers emphasized effective instruction delivery, while students valued learning outcomes, self-regulation, and engagement. This divergence highlights the unique challenges each group faces. Findings by Dennen et al. ( 2007 ) corroborate this, showing instructors focusing on content and guidance, while students prioritize interpersonal communication and individualized attention. Additionally, Lee et al. ( 2011 ) found that reduced transactional distance and increased student engagement led to enhanced perceptions of learning outcomes, aligning with students’ priorities in online courses. Understanding these differing perspectives is crucial for developing comprehensive online learning strategies that address the needs of both educators and learners.

Integrating these findings with broader contextual elements such as technological infrastructure, pedagogical strategies, socio-economic backgrounds, and environmental factors (Balanskat and Bingimlas 2006 ) further enriches our understanding. The interplay between these external factors and Yu’s nine key aspects forms a complex educational ecosystem. For example, government interventions and training programs have been shown to increase teachers’ enthusiasm for ICT and its routine use in education (Balanskat and Bingimlas 2006 ). Additionally, socioeconomic factors significantly impact students’ experiences with online learning, as the digital divide in connectivity and access to computers at home influences the ICT experience, an important factor for school achievement (OECD 2015 ; Punie et al. 2006 ).

In sum, the study advocates for a holistic approach to understanding and enhancing online education, recognizing the complex interplay between internal factors and external elements that shape the educational ecosystem in the digital age.

Conclusion and future research

This study offered a comprehensive exploration into the retrospective perceptions of college teachers and undergraduate students regarding their experiences with online learning following the COVID-19 pandemic. It was guided by a framework encompassing nine key factors that influence online learning outcomes. To delve into these perspectives, the research focused on three pivotal questions. These questions aimed to uncover how both undergraduates and teachers in China view the effectiveness of online learning post-pandemic, identify which of the nine influencing factors had the most significant impact, and propose recommendations for enhancing the future effectiveness of online learning.

In addressing the first research question concerning the retrospective perceptions of online learning’s effectiveness among undergraduates and teachers in China post-COVID-19 pandemic, the thematic analysis has delineated clear divergences in attitude between the two demographics. Participants were primarily divided into three categories based on their stance toward online learning: positive, neutral, and negative. The results highlighted a pronounced variance in attitude distribution between teachers and students, with a higher percentage of teachers expressing clear-cut opinions, either favorably or unfavorably, towards the effectiveness of online learning.

Conversely, students displayed a pronounced inclination towards neutrality, revealing a more cautious or mixed stance on the effectiveness of online learning. This prevalent neutrality within the student body could be attributed to a range of underlying reasons. It might signify students’ uncertainties or varied experiences with online platforms, differences in engagement levels, gaps in digital literacy, or fluctuating quality of online materials and teaching methods. Moreover, this neutral attitude may arise from the psychological and social repercussions of the pandemic, which have potentially altered students’ approaches to and perceptions of learning in an online context.

In the exploration of the nine influential factors in online learning, it was discovered that both teachers and students overwhelmingly identified instruction as the most critical aspect. This was closely followed by engagement, interaction, motivation, and other factors, while performance and satisfaction were perceived as less influential by both groups. However, the attitudes of teachers and students towards these factors revealed notable differences, particularly about instruction. Teachers often attributed challenges in online instruction to technological issues, whereas students perceived the quality of instruction as a major influence on their learning effectiveness. This dichotomy highlights the distinct perspectives arising from their different roles within the educational process.

A further divergence was observed in views on self-efficacy and self-regulation. Teachers, with a focus on delivering content, emphasized the importance of self-efficacy, while students, grappling with the demands of online learning, prioritized self-regulation. This reflects their respective positions in the online learning environment, with teachers concerned about the efficacy of their instructional strategies and students about managing their learning process. Interestingly, the study also illuminated that students did not always perceive themselves as independent learners, which contributed to the high priority they placed on instruction quality. This insight underlines a significant area for development in online learning strategies, emphasizing the need for fostering greater learner autonomy.

Notably, both teachers and students concurred that stimulating interest was a key factor in enhancing online learning. They proposed innovative approaches such as emulating popular online personalities, enhancing interactive elements, and contextualizing content to make it more relatable to students’ lives. Additionally, practical suggestions like issuing preview tasks and conducting in-class quizzes were highlighted as methods to boost student engagement and learning efficiency. The consensus on the importance of supervisory roles underscores the necessity for a balanced approach that integrates guidance and independence in the online learning environment.

The outcomes of our study highlight the multifaceted nature of online learning, accentuated by the varied perspectives and distinct needs of teachers and students. This complexity underscores the necessity of recognizing and addressing these nuances when designing and implementing online learning strategies. Furthermore, our findings offer a comprehensive overview of both the strengths and weaknesses of online learning during an unprecedented time, offering valuable insights for educators, administrators, and policy-makers involved in higher education. Moreover, it emphasized the intricate interplay of multiple factors—behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, and self-regulation—in shaping online learning outcomes. presents some limitations, notably its reliance on a single research method and a limited sample size.

However, the exclusive use of reflective diaries and interviews restricts the range of data collection methods, which might have been enriched by incorporating additional quantitative or mixed-method approaches. Furthermore, the sample, consisting only of students and teachers from one university, may not adequately represent the diverse experiences and perceptions of online learning across different educational contexts. These limitations suggest the need for a cautious interpretation of the findings and indicate areas for future research expansion. Future research could extend this study by incorporating a larger, more diverse sample to gain a broader understanding of undergraduate students’ retrospections across different contexts and cultures. Furthermore, research could also explore how to better equip students with the skills and strategies necessary to optimize their online learning experiences, especially in terms of the self-regulated learning and motivation aspects.

Data availability

The data supporting this study is available from https://doi.org/10.6084/m9.figshare.25583664.v1 . The data consists of reflective diaries from 46 Year 1 students from a comprehensive university in China and 18 college teachers. We utilized thematic analysis to interpret the reflective diaries, guided initially by nine factors. The results highlight the critical need for tailored online learning strategies and provide insights into its advantages and challenges for stakeholders in higher education.

Aguilera-Hermida AP (2020) College students’ use and acceptance of emergency online learning due to COVID-19. Int. J. Educ. Res. Open 1:100011. https://doi.org/10.1016/j.ijedro.2020.100011

Article   Google Scholar  

Al-Emran, M, & Shaalan, K (2021, October 27). E-podium technology: A medium of managing knowledge at Al Buraimi University College via M-learning. In Proceedings of the 2nd BCS International IT Conference, Abu Dhabi, United Arab Emirates 2014. Retrieved October 17, 2023, from https://dblp.uni-trier.de/rec/conf/bcsit/EmranS14.html

Ali W (2020) Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. High. Educ. Stud. 10(3):16–25. https://doi.org/10.5539/hes.v10n3p16

Al-Nasa’h M, Al-Tarawneh L, Awwad FMA, Ahmad I (2021) Estimating Students’ Online Learning Satisfaction during COVID-19: A Discriminant Analysis. Heliyon 7(12):1–7. https://doi.org/10.1016/j.helyon.2021.e08544

Al-Rahmi WM, Yahaya N, Aldraiweesh AA, Alamri MM, Aljarboa NA, Alturki U (2019) Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. IEEE Access 7:26797–26809. https://doi.org/10.1109/ACCESS.2019.2899368

Aristovnik A, Keržič D, Ravšelj D, Tomaževič N, Umek L (2021) Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability 12(20):8438. https://doi.org/10.3390/su12208438

Article   CAS   Google Scholar  

Baber H (2021) Modelling the Acceptance of E-learning during the Pandemic Of COVID-19-A Study of South Korea. Int. J. Manag. Educ. 19(2):1–15. https://doi.org/10.1016/j.ijme.2021.100503

Article   MathSciNet   Google Scholar  

Bahasoan AN, Ayuandiani W, Mukhram M, Rahmat A (2020) Effectiveness of online learning in pandemic COVID-19. Int. J. Sci., Technol. Manag. 1(2):100–106

Google Scholar  

Balanskat A, Bingimlas KA (2006) Barriers to the Successful Integration of ICT in Teaching and Learning Environments: A Review of the Literature. Eurasia J. Math. Sci. Technol. Educ. 5(3):235–245. https://doi.org/10.12973/ejmste/75275

Bao W (2020) COVID-19 and Online Teaching in Higher Education: A Case Study of Peking University. Hum. Behav. Emerg. Technol. 2(2):113–115. https://doi.org/10.1002/hbe2.191

Article   PubMed   PubMed Central   Google Scholar  

Bernard RM et al. (2004) How Does Distance Education Compare with Classroom Instruction? A Meta-Analysis of the Empirical Literature. Rev. Educ. Res. 74(3):379–439

Boling EC, Hough M, Krinsky H, Saleem H, Stevens M (2012) Cutting the distance in distance education: Perspectives on what promotes positive, online learning experiences. Internet High. Educ. 15:118–126. https://doi.org/10.1016/j.iheduc.2011.11.006

Bozkurt A, Sharma RC (2020) Emergency remote teaching in a time of global crisis due to Coronavirus pandemic. Asian J. Distance Educ. 15(1):i–vi

Broadbent J (2017) Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High. Educ. 33:24–32. https://doi.org/10.1016/j.iheduc.2017.01.004

Calaguas NP, Consunji PMP (2022) A Structural Equation Model Predicting Adults’ Online Learning Self-efficacy. Educ. Inf. Technol. 27:6233–6249. https://doi.org/10.1007/s10639-021-10871-y

Careaga-Butter M, Quintana MGB, Fuentes-Henríquez C (2020) Critical and Prospective Analysis of Online Education in Pandemic and Post-pandemic Contexts: Digital Tools and Resources to Support Teaching in synchronous and Asynchronous Learning Modalities. Aloma: evista de. psicologia, ciències de. l’educació i de. l’esport Blanquerna 38(2):23–32

Chakraborty M, Muyia Nafukho F (2014) Strengthening Student Engagement: What do Students Want in Online Courses? Eur. J. Train. Dev. 38(9):782–802

Chang V (2016) Review and Discussion: E-learning for Academia and Industry. Int. J. Inf. Manag. 36(3):476–485. https://doi.org/10.1016/j.ijinfomgt.2015.12.007

Chen YJ, Hsu LW (2022) Enhancing EFL Learners’ Self-efficacy Beliefs of Learning English with emoji Feedbacks in Call: Why and How. Behav. Sci. 12(7):227. https://doi.org/10.3390/bs12070227

Chuenyindee T, Montenegro LD, Ong AKS, Prasetyo YT, Nadlifatin R, Ayuwati ID, Sittiwatethanasiri T, Robas KPE (2022) The Perceived Usability of the Learning Management System during the COVID-19 Pandemic: Integrating System Usability Scale, Technology Acceptance Model, and Task-technology Fit. Work 73(1):41–58. https://doi.org/10.3233/WOR-220015

Article   PubMed   Google Scholar  

Crawford J, Butler-Henderson K, Rudolph J, Malkawi B, Glowatz M, Burton R, Lam S (2020) COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. J. Appl. Teach. Learn. 3(1):120. https://doi.org/10.37074/jalt.2020.3.1.7

Dennen VP (2014) Becoming a blogger: Trajectories, norms, and activities in a community of practice. Computers Hum. Behav. 36:350–358. https://doi.org/10.1016/j.chb.2014.03.028

Dennen VP, Darabi AA, Smith LJ (2007) Instructor–learner Interaction in Online Courses: The Relative Perceived Importance of Particular Instructor Actions on Performance and Satisfaction. Distance Educ. 28(1):65–79

Dolan E, Hancock E, Wareing A (2015) An evaluation of online learning to teach practical competencies in undergraduate health science students. Internet High. Educ. 24:21–25

Garrison DR (2003) Cognitive Presence for Effective Asynchronous Online Learning: The Role of Reflective. Inq., Self-Direction Metacognition. Elem. Qual. Online Educ.: Pract. Direction 4(10):47–58

Gavranović V, Prodanović M (2021) ESP Teachers’ Perspectives on the Online Teaching Environment Imposed in the Covid-19 Era-A Case Study. N. Educ. Rev. 2:188–197. https://doi.org/10.15804/TNER.2021.64.2.15

Greenhow C, Lewin C, Staudt Willet KB (2021) The Educational Response to Covid-19 across Two Countries: a Critical Examination of Initial Digital Pedagogy Adoption. Technol., Pedagog. Educ. 30(1):7–25

Händel M, Stephan M, Gläser-Zikuda M, Kopp B, Bedenlier S, Ziegler A (2020) Digital readiness and its effects on higher education students’ socio-emotional perceptions in the context of the COVID-19 pandemic. J. Res. Technol. Educ. 53(2):1–13

Harper B (2018) Technology and Teacher-student Interactions: A Review of Empirical Research. J. Res. Technol. Educ. 50(3):214–225

Article   ADS   Google Scholar  

Hernández-Lara AB, Serradell-López E (2018) Student interactions in online discussion forums: their perception on learning with business simulation games. Behav. Inf. Technol. 37(4):419–429

Hodges C, Moore S, Lockee B, Trust T, Bond A (2020) The difference between emergency remote teaching and online learning. Educause Rev. 27:1–12

Hongsuchon T, Emary IMME, Hariguna T, Qhal EMA (2022) Assessing the Impact of Online-learning Effectiveness and Benefits in Knowledge Management, the Antecedent of Online-learning Strategies and Motivations: An Empirical Study. Sustainability 14(5):1–16. https://doi.org/10.3390/su14052570 . (2020)

Johnson N, Veletsianos G, Seaman J (2020) US faculty and administrators’ experiences and approaches in the early weeks of the COVID-19 pandemic. Online Learn. 24(2):6–21

Khojasteh L, Karimian Z, Farahmandi AY, Nasiri E, Salehi N (2023) E-content Development of English Language Courses during COVID-19: A Comprehensive Analysis of Students’ Satisfaction. J. Computer Educ. 10(1):107–133. https://doi.org/10.1007/s40692-022-00224-0

Kohnke, L, & Foung, D (2023). Exploring Microlearning for Teacher Professional Development: Voices from Hong Kong. In Tafazoli, D, M Picard (Eds.). Handbook of CALL Teacher Education Professional Development (pp. 279-292). Singapore: Springer Nature Singapore Pte Ltd

Lai CH, Lin HW, Lin RM, Tho PD (2019) Effect of Peer Interaction among Online Learning Community on Learning Engagement and Achievement. Int. J. Distance Educ. Technol. (IJDET) 17(1):66–77

Lashley PM, Sobers NP, Campbell MH, Emmanuel MK, Greaves N, Gittens-St Hilaire M, Murphy MM, Majumder MAA (2022) Student Satisfaction and Self-Efficacy in a Novel Online Clinical Clerkship Curriculum Delivered during the COVID-19 Pandemic. Adv. Med. Educ. Pract. 13:1029–1038. https://doi.org/10.2147/AMEP.S374133

Lee SJ, Srinivasan S, Trail T, Lewis D, Lopez S (2011) Examining the Relationship among Student Perception of Support, Course Satisfaction, and Learning Outcomes in Online Learning. Internet High. Educ. 14(3):158–163

Lowenthal PR, Dunlap JC (2020) Social Presence and Online Discussions: A Mixed Method Investigation. Distance Educ. 41:490–514. https://doi.org/10.1080/01587919.2020.1821603

Lucas M, Vicente PN (2023) A Double-edged Sword: Teachers’ Perceptions of the Benefits and Challenges of Online Learning and Learning in Higher Education. Educ. Inf. Technol. 23:5083–5103. https://doi.org/10.1007/s10639-022-11363-3

Maatuk AM, Elberkawi EK, Aljawarneh S, Rashaideh H, Alharbi H (2022) The COVID-19 Pandemic and E-learning: Challenges and Opportunities from the Perspective of Students and Instructors. J. Comput. High. Educ. 34:21–38. https://doi.org/10.1007/s12528-021-09274-2

Marinoni G, Van’t Land H, Jensen T (2020) The Impact of Covid-19 on Higher Education around the World. IAU Glob. Surv. Rep. 23:1–17

Martin F, Bolliger DU (2018) Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learn. 22(1):205–222

Means B, et al. (2010). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington, D. C.: U.S. Department of Education

Miao J, Ma L (2022) Students’ Online Interaction, Self-regulation, and Learning Engagement in Higher Education: The Importance of Social Presence to Online Learning. Front. Psychol. 13:1–9. https://doi.org/10.3389/fpsyg.2022.815220

Mulla T, Munir S, Mohan V (2023) An Exploratory Study to Understand Faculty Members’ Perceptions and Challenges in Online Teaching. Int. Rev. Educ. 69:73–99. https://doi.org/10.1007/s11159-023-100

Ng EW (2018) Integrating Self-regulation Principles with Flipped Classroom Pedagogy for First Year University Students. Computer Educ. 126:65–74. https://doi.org/10.1007/s11409-011-9082-8

Ong SGT, Quek GCL (2023) Enhancing teacher–student interactions and student online engagement in an online learning environment. Learn. Environ. Res. 26:681–707. https://doi.org/10.1007/s10984-022-09447-5

Organisation for Economic Co‑operation and Development. (2015). The g20 skills strategy for developing and using skills for the 21st century. Retrieved from https://www.oecd.org/g20/topics/employment-andsocial-policy/The-G20-Skills-Strategy-for-Developing-and-Using-Skills-for-the-21st-Century.pdf

Paechter M, Maier B (2010) Online or face-to-face? Students’ experiences and preferences in e-learning. internet High. Educ. 13(4):292–297

Panigrahi R, Srivastava PR, Sharma D (2018) Online Learning: Adoption, Continuance, and Learning Outcome—A Review of Literature. Int. J. Inf. Manag. 43:1–14

Paul C, Diana M (2006) Teaching Methods and Time on Task in Junior Classrooms. Educ. Res. 30:90–97

Punie, Y, Zinnbauer, D, & Cabrera, M (2006). A review of the impact of ICT on learning. European Commission, Brussels, 6(5), 635-650

Radmer F, Goodchild S (2021) Online Mathematics Teaching and Learning during the COVID-19 Pandemic: The Perspective of Lecturers and Students. Nord. J. STEM Educ. 5(1):1–5. https://doi.org/10.5324/njsteme.v5i1.3914

Rapanta C, Botturi L, Goodyear P, Guadia L, Koole M (2021) Balancing Technology, Pedagogy and the New Normal: Post-pandemic Challenges for Higher Education. Postdigital Sci. Educ. 3:715–742. https://doi.org/10.1007/s42438-021-00249-1

Rasheed, RA, Kamsin, A, & Abdullah, NA (2020) Challenges in the Online Component of Blended Learning: A Systematic Review. Computers & Education, 144. https://doi.org/10.1016/j.compedu.2019.103701

Schaufeli W, Salanova M, Gonzalez-Roma V (2002) The Measurement of Engagement and Burnout: A Two Sample Confirmatory Factor Analytic Approach. J. Happiness Stud. 3:71–92

Song L, Singleton ES, Hill JR, Koh MH (2004) Improving online learning: Student perceptions of useful and challenging characteristics. Internet High. Educ. 7(1):59–70. https://doi.org/10.1016/j.iheduc.2003.11.003

Tan HT, Chan PP, Said NM (2021) Higher Education Students’ Online Instruction Perceptions: A Quality Virtual Learning Environment. Sustainability 13:10840. https://doi.org/10.3390/su131910840

Tenório T, Bittencourt II, Isotani S, Silva AP (2016) Does peer assessment in online learning environments work? A systematic review of the literature. Computers Hum. Behav. 64:94–107. https://doi.org/10.1016/j.chb.2016.06.020

Torsani, S (2023) Teacher Education in Mobile Assisted Language Learning for Adult Migrants: A Study of Provincial Centers for Adult Education in Italy. In Tafazoli, D, & M Picard (eds.). Handbook of CALL Teacher Education Professional Development (pp. 179-192). Singapore: Springer Nature Singapore Pte Ltd

Trust T, Whalen J (2020) Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 pandemic. J. Technol. Teach. Educ. 28(2):189–199

UNESCO (2020a) COVID-19 Impact on education. UNESCO. Retrieved from https://en.unesco.org/covid19/educationresponse

UNESCO (2020b) Education: From Disruption to Recovery. UNESCO. Retrieved from https://en.unesco.org/covid19/educationresponse

Wang M, & Wang F (2021, August) Comparative Analysis of University Education Effect under the Traditional Teaching and Online Teaching Mode. In The Sixth International Conference on Information Management and Technology (pp. 1-6)

Xu Q, Wu J, Peng HY (2022) Chinese EFL University Students’ Self-efficacy for Online Self-regulated Learning: Dynamic Features and Influencing Factors. Front. Psychol. 13:1–12. https://doi.org/10.3389/fpsyg.2022.912970

Zheng RK, Li F, Jiang L, Li SM (2022) Analysis of the Current Situation and Driving Factors of College Students’ Autonomous Learning in the Network Environment. Front. Humanit. Soc. Sci. 2(7):44–50

Zhu XM, Gong Q, Wang Q, He YJ, Sun ZQ, Liu FF (2023) Analysis of Students’ Online Learning Engagement during the COVID-19 Pandemic: A Case Study of a SPOC-Based Geography Education Undergraduate Course. Sustainability 15(5):4544. https://doi.org/10.3390/su15054544

Zhu Y, Geng G, Disney L, Pan Zihao (2023) Changes in University Students’ Behavioral intention to learn online throughout the COVID-19: Insights for Online Teaching in the Post-pandemic Era. Educ. Inf. Technol. 28:3859–3892. https://doi.org/10.1007/s10639-022-11320-0

Zhu YH, Xu YY, Wang XY, Yan SY, Zhao L (2022) The Selectivity and Suitability of Online Learning Resources as Predictor of the Effects of Self-efficacy on Teacher Satisfaction During the COVID-19 Lockdown. Front. Psychol. 13:1–11. https://doi.org/10.3389/fpsyg.2022.765832

Zimmerman WA, Kulikowich JM (2016) Online Learning Self-efficacy in Students with and Without Online Learning Experience. Am. J. Distance Educ. 30(3):180–190. https://doi.org/10.1080/08923647.2016.1193801

Download references

Acknowledgements

The Corresponding author received the National Social Science Foundation of China for Education General Program (BGA210054) for this work.

Author information

Authors and affiliations.

School of Foreign Studies, Wenzhou University, Wenzhou, China

Yujie Su, Xiaoshu Xu & Xinyu Xu

Faculty of Languages and Translation, Macao Polytechnic University, Macao, China

Yunfeng Zhang

Faculty of Applied Sciences, Macao Polytechnic University, Macao, China

Shanshan Hao

You can also search for this author in PubMed   Google Scholar

Contributions

XSX was responsible for conceptualization and, alongside YFZ, for data curation. YJS and XYX conducted the formal analysis. Funding acquisition was managed by YFZ. The investigation was carried out by YJS and YFZ. Methodology development was a collaboration between YJS and XSX. XSX and YJS also managed project administration, with additional resource management by SSH and XYX. YJS handled the software aspect, and supervision was overseen by XSX. SSH and XYX were responsible for validation, and visualization was managed by YJS. The original draft was written by XSX and YJS, while the review and editing were conducted by YFZ and SSH.

Corresponding author

Correspondence to Xiaoshu Xu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

The questionnaire and methodology for this study were approved by the Human Research Ethics Committee of Wenzhou University (Ethics approval number WGY202302).

Informed consent

Informed consent was diligently obtained from all participants involved in the study, ensuring adherence to ethical standards. Detailed consent forms, outlining the study’s scope and participants’ rights, were signed by participants. Documentation of this process is well-maintained and can be provided upon request during peer review or post-publication.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Su, Y., Xu, X., Zhang, Y. et al. Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors. Humanit Soc Sci Commun 11 , 594 (2024). https://doi.org/10.1057/s41599-024-03097-z

Download citation

Received : 24 October 2023

Accepted : 17 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1057/s41599-024-03097-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Research Skills

50 Mini-Lessons For Teaching Students Research Skills

Please note, I am no longer blogging and this post hasn’t updated since April 2020.

For a number of years, Seth Godin has been talking about the need to “ connect the dots” rather than “collect the dots” . That is, rather than memorising information, students must be able to learn how to solve new problems, see patterns, and combine multiple perspectives.

Solid research skills underpin this. Having the fluency to find and use information successfully is an essential skill for life and work.

Today’s students have more information at their fingertips than ever before and this means the role of the teacher as a guide is more important than ever.

You might be wondering how you can fit teaching research skills into a busy curriculum? There aren’t enough hours in the day! The good news is, there are so many mini-lessons you can do to build students’ skills over time.

This post outlines 50 ideas for activities that could be done in just a few minutes (or stretched out to a longer lesson if you have the time!).

Learn More About The Research Process

I have a popular post called Teach Students How To Research Online In 5 Steps. It outlines a five-step approach to break down the research process into manageable chunks.

This post shares ideas for mini-lessons that could be carried out in the classroom throughout the year to help build students’ skills in the five areas of: clarify, search, delve, evaluate , and cite . It also includes ideas for learning about staying organised throughout the research process.

Notes about the 50 research activities:

• These ideas can be adapted for different age groups from middle primary/elementary to senior high school.
• Many of these ideas can be repeated throughout the year.
• Depending on the age of your students, you can decide whether the activity will be more teacher or student led. Some activities suggest coming up with a list of words, questions, or phrases. Teachers of younger students could generate these themselves.
• Depending on how much time you have, many of the activities can be either quickly modelled by the teacher, or extended to an hour-long lesson.
• Some of the activities could fit into more than one category.
• Looking for simple articles for younger students for some of the activities? Try DOGO News or Time for Kids . Newsela is also a great resource but you do need to sign up for free account.
• Why not try a few activities in a staff meeting? Everyone can always brush up on their own research skills!

• Choose a topic (e.g. koalas, basketball, Mount Everest) . Write as many questions as you can think of relating to that topic.
• Make a mindmap of a topic you’re currently learning about. This could be either on paper or using an online tool like Bubbl.us .
• Read a short book or article. Make a list of 5 words from the text that you don’t totally understand. Look up the meaning of the words in a dictionary (online or paper).
• Look at a printed or digital copy of a short article with the title removed. Come up with as many different titles as possible that would fit the article.
• Come up with a list of 5 different questions you could type into Google (e.g. Which country in Asia has the largest population?) Circle the keywords in each question.
• Write down 10 words to describe a person, place, or topic. Come up with synonyms for these words using a tool like  Thesaurus.com .
• Write pairs of synonyms on post-it notes (this could be done by the teacher or students). Each student in the class has one post-it note and walks around the classroom to find the person with the synonym to their word.

• Explore how to search Google using your voice (i.e. click/tap on the microphone in the Google search box or on your phone/tablet keyboard) . List the pros and cons of using voice and text to search.
• Open two different search engines in your browser such as Google and Bing. Type in a query and compare the results. Do all search engines work exactly the same?
• Have students work in pairs to try out a different search engine (there are 11 listed here ). Report back to the class on the pros and cons.
• Think of something you’re curious about, (e.g. What endangered animals live in the Amazon Rainforest?). Open Google in two tabs. In one search, type in one or two keywords ( e.g. Amazon Rainforest) . In the other search type in multiple relevant keywords (e.g. endangered animals Amazon rainforest).  Compare the results. Discuss the importance of being specific.
• Similar to above, try two different searches where one phrase is in quotation marks and the other is not. For example, Origin of “raining cats and dogs” and Origin of raining cats and dogs . Discuss the difference that using quotation marks makes (It tells Google to search for the precise keywords in order.)
• Try writing a question in Google with a few minor spelling mistakes. What happens? What happens if you add or leave out punctuation ?
• Try the AGoogleADay.com daily search challenges from Google. The questions help older students learn about choosing keywords, deconstructing questions, and altering keywords.
• Explore how Google uses autocomplete to suggest searches quickly. Try it out by typing in various queries (e.g. How to draw… or What is the tallest…). Discuss how these suggestions come about, how to use them, and whether they’re usually helpful.
• Watch this video  from Code.org to learn more about how search works .
• Take a look at  20 Instant Google Searches your Students Need to Know  by Eric Curts to learn about “ instant searches ”. Try one to try out. Perhaps each student could be assigned one to try and share with the class.
• Experiment with typing some questions into Google that have a clear answer (e.g. “What is a parallelogram?” or “What is the highest mountain in the world?” or “What is the population of Australia?”). Look at the different ways the answers are displayed instantly within the search results — dictionary definitions, image cards, graphs etc.

• Watch the video How Does Google Know Everything About Me?  by Scientific American. Discuss the PageRank algorithm and how Google uses your data to customise search results.
• Brainstorm a list of popular domains   (e.g. .com, .com.au, or your country’s domain) . Discuss if any domains might be more reliable than others and why (e.g. .gov or .edu) .
• Discuss (or research) ways to open Google search results in a new tab to save your original search results  (i.e. right-click > open link in new tab or press control/command and click the link).
• Try out a few Google searches (perhaps start with things like “car service” “cat food” or “fresh flowers”). A re there advertisements within the results? Discuss where these appear and how to spot them.
• Look at ways to filter search results by using the tabs at the top of the page in Google (i.e. news, images, shopping, maps, videos etc.). Do the same filters appear for all Google searches? Try out a few different searches and see.
• Type a question into Google and look for the “People also ask” and “Searches related to…” sections. Discuss how these could be useful. When should you use them or ignore them so you don’t go off on an irrelevant tangent? Is the information in the drop-down section under “People also ask” always the best?
• Often, more current search results are more useful. Click on “tools” under the Google search box and then “any time” and your time frame of choice such as “Past month” or “Past year”.
• Have students annotate their own “anatomy of a search result” example like the one I made below. Explore the different ways search results display; some have more details like sitelinks and some do not.

• Find two articles on a news topic from different publications. Or find a news article and an opinion piece on the same topic. Make a Venn diagram comparing the similarities and differences.
• Choose a graph, map, or chart from The New York Times’ What’s Going On In This Graph series . Have a whole class or small group discussion about the data.
• Look at images stripped of their captions on What’s Going On In This Picture? by The New York Times. Discuss the images in pairs or small groups. What can you tell?
• Explore a website together as a class or in pairs — perhaps a news website. Identify all the advertisements .
• Have a look at a fake website either as a whole class or in pairs/small groups. See if students can spot that these sites are not real. Discuss the fact that you can’t believe everything that’s online. Get started with these four examples of fake websites from Eric Curts.
• Give students a copy of my website evaluation flowchart to analyse and then discuss as a class. Read more about the flowchart in this post.
• As a class, look at a prompt from Mike Caulfield’s Four Moves . Either together or in small groups, have students fact check the prompts on the site. This resource explains more about the fact checking process. Note: some of these prompts are not suitable for younger students.
• Practice skim reading — give students one minute to read a short article. Ask them to discuss what stood out to them. Headings? Bold words? Quotes? Then give students ten minutes to read the same article and discuss deep reading.

All students can benefit from learning about plagiarism, copyright, how to write information in their own words, and how to acknowledge the source. However, the formality of this process will depend on your students’ age and your curriculum guidelines.

• Watch the video Citation for Beginners for an introduction to citation. Discuss the key points to remember.
• Look up the definition of plagiarism using a variety of sources (dictionary, video, Wikipedia etc.). Create a definition as a class.
• Find an interesting video on YouTube (perhaps a “life hack” video) and write a brief summary in your own words.
• Have students pair up and tell each other about their weekend. Then have the listener try to verbalise or write their friend’s recount in their own words. Discuss how accurate this was.
• Read the class a copy of a well known fairy tale. Have them write a short summary in their own words. Compare the versions that different students come up with.
• Try out MyBib — a handy free online tool without ads that helps you create citations quickly and easily.
• Give primary/elementary students a copy of Kathy Schrock’s Guide to Citation that matches their grade level (the guide covers grades 1 to 6). Choose one form of citation and create some examples as a class (e.g. a website or a book).
• Make a list of things that are okay and not okay to do when researching, e.g. copy text from a website, use any image from Google images, paraphrase in your own words and cite your source, add a short quote and cite the source. 
• Have students read a short article and then come up with a summary that would be considered plagiarism and one that would not be considered plagiarism. These could be shared with the class and the students asked to decide which one shows an example of plagiarism .
• Older students could investigate the difference between paraphrasing and summarising . They could create a Venn diagram that compares the two.
• Write a list of statements on the board that might be true or false ( e.g. The 1956 Olympics were held in Melbourne, Australia. The rhinoceros is the largest land animal in the world. The current marathon world record is 2 hours, 7 minutes). Have students research these statements and decide whether they’re true or false by sharing their citations.

Staying Organised

• Make a list of different ways you can take notes while researching — Google Docs, Google Keep, pen and paper etc. Discuss the pros and cons of each method.
• Learn the keyboard shortcuts to help manage tabs (e.g. open new tab, reopen closed tab, go to next tab etc.). Perhaps students could all try out the shortcuts and share their favourite one with the class.
• Find a collection of resources on a topic and add them to a Wakelet .
• Listen to a short podcast or watch a brief video on a certain topic and sketchnote ideas. Sylvia Duckworth has some great tips about live sketchnoting
• Learn how to use split screen to have one window open with your research, and another open with your notes (e.g. a Google spreadsheet, Google Doc, Microsoft Word or OneNote etc.) .

All teachers know it’s important to teach students to research well. Investing time in this process will also pay off throughout the year and the years to come. Students will be able to focus on analysing and synthesizing information, rather than the mechanics of the research process.

By trying out as many of these mini-lessons as possible throughout the year, you’ll be really helping your students to thrive in all areas of school, work, and life.

Also remember to model your own searches explicitly during class time. Talk out loud as you look things up and ask students for input. Learning together is the way to go!

You Might Also Enjoy Reading:

How To Evaluate Websites: A Guide For Teachers And Students

Five Tips for Teaching Students How to Research and Filter Information

Typing Tips: The How and Why of Teaching Students Keyboarding Skills

8 Ways Teachers And Schools Can Communicate With Parents

10 Replies to “50 Mini-Lessons For Teaching Students Research Skills”

Loving these ideas, thank you

This list is amazing. Thank you so much!

So glad it’s helpful, Alex! 🙂

Hi I am a student who really needed some help on how to reasearch thanks for the help.

So glad it helped! 🙂

seriously seriously grateful for your post. 🙂

So glad it’s helpful! Makes my day 🙂

How do you get the 50 mini lessons. I got the free one but am interested in the full version.

Hi Tracey, The link to the PDF with the 50 mini lessons is in the post. Here it is . Check out this post if you need more advice on teaching students how to research online. Hope that helps! Kathleen

Best wishes to you as you face your health battler. Hoping you’ve come out stronger and healthier from it. Your website is so helpful.

Comments are closed.

Along with Stanford news and stories, show me:

• Student information
• Faculty/Staff information

We want to provide announcements, events, leadership messages and resources that are relevant to you. Your selection is stored in a browser cookie which you can remove at any time using “Clear all personalization” below.

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

• Plan for College and Career
• Take the ACT
• School and District Assessment
• Career-Ready Solutions
• Students & Parents
• Open Search Form
• ACT Research
• All Services and Resources
• Growth Modeling Resources
• Data and Visualization
• All Reports
• ACT Research Publications
• Technical Documentation
• Studies and Partnerships
• About ACT Research
• Meet Our Experts
• Get Research Updates

Other ACT Services and Products

New Featured May 13, 2024

Social and Emotional Learning Is Associated With Students Hard Work

Social and emotional learning (SEL) is known to have positive effects on students’ social and emotional skills (Mahoney et al., 2008). We sought to determine if the efficacy of SEL could be detected with single-item predictor and criterion variables.

Size: 175.5KB | Pages: 3

This action will open a new window. Do you want to proceed?

Welcome to ACT

If you are accessing this site from outside the United States, Puerto Rico, or U.S. Territories, please proceed to the non-U.S. version of our website.

• Skip to main content
• Keyboard shortcuts for audio player

Your Health

• Treatments & Tests
• Health Inc.
• Public Health

Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

Is this some kind of joke? A school facing shortages starts teaching standup comedy

In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

Shots - Health News

Feeling artsy here's how making art helps your brain.

"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

• handwriting

In an increasingly digital world, it’s more important than ever for students to develop their handwriting skills

By Amra Pajalic

• X (formerly Twitter)

These days, students are often required to bring a device to school to access equitable education. But as they're increasingly using more and more technology, their handwriting is being affected. Some of you might wonder why this is an issue.

When we look at the assessment tools we use, such as end of semester tests or high school exams to allocate an ATAR for university entrance, students must undertake these in handwritten settings to authenticate their knowledge and learning.

These exams run for two to three hours and require students to produce written tasks under timed conditions. If examiners cannot read and assess a handwritten piece, this can affect scores, so handwriting legibility and stamina are crucial to ensure a student's success.

Messy and illegible handwriting has contributed to a surge in VCE students seeking special arrangements in exams, such as typing.

Another issue I've noticed cropping up due to an over-reliance on technology is the effect on grammar and punctuation, with students using online abbreviations within their responses and not knowing how to punctuate sentences correctly, thus affecting clarity of expression.

I also believe there are higher incidences of spelling mistakes because of an over-reliance on spellchecking software .

These days, in my year 9 English classes, students take notes and usually do all their homework on their computers, rarely needing to engage in handwriting. And it makes sense, because it's so convenient and easy.

But in doing so, students are missing out on many benefits of handwriting.

The benefits of handwriting

Studies have shown that handwriting supports better retention and recall of information.

A 2021 study investigating the cognitive effort of handwriting and typing found that "handwriting led to better recall than typing, particularly with the longest lists of words".

Research studies have also found that the process of handwriting, rather than typing, enhances memory and improves spelling skills.

Students who struggle with handwriting produce shorter pieces and a lower quality of content because they have less opportunity to think creatively. When students build up their handwriting fluency, it frees up their working memory and they can then instead spend more time thinking about how to plan and compose their texts.

This is something I've seen firsthand with students I teach. Students who struggle with handwriting experience a crisis of self-confidence and spend more time procrastinating than writing.

However, when they have the chance to practise a style of writing repeatedly, they develop muscle memory and can produce higher quality texts and manage their time more effectively.

These motor and cognitive benefits can lead to greater academic success.

While we might think that difficulty with handwriting and expression might not lead to long-term consequences beyond final exams and high school, we can't deny that people still use handwriting in various everyday tasks, such as filling out forms or making handwritten lists, which can be challenging if someone lacks handwriting skills.

Then there is the real possibility that it can affect professional communication. In fields where handwritten documentation, correspondence or notes are required, such as healthcare or legal professions, poor handwriting can lead to errors or misinterpretations.

Even in industries such as retail or hospitality, there is the need to handwrite signs; label stock or inventory; or produce menus. Spelling mistakes and punctuation errors can lead to low consumer confidence.

How to build children's confidence with handwriting

So how do we balance out the use of technology to support students' handwriting practice?

Parents can support their children by creating opportunities for practice at home. Start by providing handwriting tools — notebooks, pencils and erasers — and consider seeking help if your child is struggling significantly, whether from a teacher, occupational therapist or professionals who specialise in handwriting development.

As a parent, I praised my daughter's efforts in writing stories, or when she penned sweet notes to me and her father when she was angling for us to buy her something, or her forays into journaling and daily organisers.

Teachers can create different learning experiences through collaboration, games or note-making to develop students' handwriting and stamina.

As an English teacher, I used the method of the Writer's Notebook , a tried-and-true method that educators have successfully used for decades.

Teachers integrate these Writer Notebooks into the curriculum at least once a week, using them to teach and practice sentence construction and long-form pieces. These notebooks are used to demonstrate the practice of skills and the development of writing a piece.

My students' first activity of the year is always a letter to the teacher.

First, I write them a letter telling them about myself, my cultural background, the origin or story of my name, my passions and my goals, and they then write a letter to me modelling the same structure.

These letters serve a two-fold purpose. They allow me to develop a rapport with students by getting to know them on a personal level, and they help me identify their writing strengths and areas of development so I can include a goal-setting report in my reply.

As a teacher, I've had the experience of using Writer Notebooks for five years now and can unequivocally testify to their efficacy. I love sharing these at parent-teacher interviews and being able to show the progression from the beginning of the year to the end.

And most importantly, I love seeing the confidence that students themselves display in being able to write under timed conditions and having the writing stamina for exam conditions. Amra Pajalic is a teacher of 11 years. She is an award-winning author of Sabiha's Dilemma, Alma's Loyalty and Jesse's Triumph, the first three books in her own-voices young adult Sassy Saints Series.

Writing memoir can help young people with their self-expression and self-worth

BTN: Write or type, that is the question!

Cursive handwriting

University of Minnesota Twin Cities

University digital conservancy, breaking down barriers: academic obstacles of first-generation students at research universities, view/download file, persistent link to this item, journal title, journal issn, volume title, published date, description, collections, series/report number, funding information, isbn identifier, doi identifier, previously published citation, suggested citation.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use . Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories .