literature review on collaborative learning

ORIGINAL RESEARCH article

The impact of collaborative learning and personality on satisfaction in innovative teaching context.

• 1 Institute of Technology Management, National Chung Hsing University, Taichung City, Taiwan
• 2 Department of Information Management, Tunghai University, Taichung City, Taiwan

Flipped teaching is one of the most popular innovative teaching methods which has attracted a lot of attention and lead to amount of discussion in recent years. Many educators have generally encountered same doubt when implementing flipped education: Is this kind of teaching mode only applicable to students with high learning achievements? In addition, collaborative learning is often applied in flip teaching and it is also an issue worth to explore. In this study, both quantitative and qualitative studies were conducted to examine the potential factors in affecting the learners’ satisfaction in flipped education. The survey results from 171 participants showed that collaborative learning and need for cognition are significant predictors of learning satisfaction. In addition, a deeper look at the collaborative learning process was further examined by conducting deep interview. A total of 12 students from 6 different flipped-teaching courses participated the interview. The findings suggested that arranging some activities to encourage students to know each other before class that helps students find corresponding group and facilitates their expertise for collaborative learning. The mechanism significantly influenced team members’ engagement, discussion atmosphere, and efficiency. In addition, when learning tasks diversity, it will also enhance students’ innovative ability, empathy, and even promote mutual learning.

Introduction

In recent years, flipped teaching has attracted considerable attention and aroused widespread discussion. Since 2011, the search trend of relevant keywords on Google has increased exponentially ( Abeysekera and Dawson, 2015 ). Many studies have compared student performance before and after the implementation of flipped teaching, evidencing that flipped teaching can help improve academic performance ( O'flaherty and Phillips, 2015 ). When the science control system of the Department of Mechanical Engineering of Seattle University implemented flipped teaching, the course divided students into two different groups: the traditional learning method group and the flipped learning method group. Results show that the group receiving flipped teaching generally performed better in tests and examinations and had a higher degree of mastery of design issues ( Mason et al., 2013 ). A course on renal drug therapy conducted flipped teaching to evaluate its impact on students’ professional performance. The results show that compared with the previous year’s performance in a traditional classroom environment, students’ performance in the final exam improved significantly ( Pierce and Fox, 2012 ). Most of the topics discussed in the existing literature mainly focus on the comparison of the effectiveness and acceptance of flipped teaching and traditional teaching among students.

After a systematic review of research related to flipped teaching, O'flaherty and Phillips (2015) pointed out that individual differences can be explored in the future, for example, whether there are specific demographics or personalities that can predict students’ responses to flipped lessons. To explore the individual differences of students under flipped teaching, this study refers to the research of Abeysekera and Dawson (2015) . Although the research on flipped teaching has been conducted in a variety of domains ( Chang and Hwang, 2018 ; Lee and Wallace, 2018 ; Javier et al., 2020 ), this study aimed at examining the potential factors in affecting the learning satisfaction in flipped education by combining both qualitative and quantitative study. Thus, the first objective of this study is to examine the influential factors of learners’ satisfaction from the perspective of personality, self-efficacy, and collaborative learning. Specifically, this study focuses on the impact of students’ personality traits and collaborative learning on learning satisfaction under flipped teaching so as to understand the response of individual differences to flipped teaching. This study uses the cognitive needs theory and learning self-efficacy as the entry point to explore personality traits and provides a reference for educators who plan to practice in flipped teaching in the future.

Further, as most of the flipped teaching courses require students form into groups and collaborative with each other to finish the projects, the second objective of this study is to look deeper into the collaborative learning process for students participating the flipped learning. Thus, deep interviews were conducted to understand the collaborative learning process when the students participated the flipped learning and the findings can provide significant insight for educators who want to teach in a more innovative way and increase students’ engagement in flipped teaching.

Literature Review

Flipped teaching.

The term “flipped teaching” is commonly used to describe a teaching method wherein the completion of homework after class is carried out in the classroom and the classwork is to be completed by the students themselves before class ( Abeysekera and Dawson, 2015 ). The idea of flipped teaching first occurred as an accidental discovery by a high school chemistry teacher in the United States when he wanted to conduct remedial classes for absent students. He bought a set of software and uploaded the classwork teachings on the Internet so that absent students could keep up with their studies. However, in addition to the students who were absent from class, students who had originally attended the class also used the online teaching resources to review the course content and benefited from it. This discovery made Bergmann and Sams rethink the allocation of class time in the teaching process ( Tucker, 2012 ).

Many studies have proven that the flipped teaching method can improve students’ learning motivation. In a statistics course at a university, it is understood through interviews that students are more willing to accept collaborative learning and innovative teaching methods than traditional teaching ( Strayer, 2012 ). After the introduction of flipped teaching in the British chemistry curriculum, students expressed that they preferred this interactive mode, because it gave them more opportunities to develop more advanced learning skills in the classroom than before ( Yeung, 2014 ). It not only improves learning motivation but also stimulates students’ active learning because of the changes in the teaching process. In addition to the teaching content of the course itself, when the course is conducted in the form of group discussions, communication and critical thinking abilities also improve. In a study on the implementation of flipped teaching in nursing courses, students had more opportunities to discuss and solve unfamiliar problems with their peers and teachers in the classroom. Through the redesigned curriculum, students were required to criticize various scenarios, collect information, and provide insights for patients. Such learning activities combine knowledge of patient assessment, critical thinking, and evaluation skills ( Ferreri and O’connor, 2013 ).

The European Higher Education Framework proposes a shift from the previous one-way teaching of courses by teachers to a student-centered learning approach ( Schreurs and Dumbraveanu, 2014 ). Honeycutt and Garrett (2014) refer to the flipped classroom as paying attention to a learner’s learning status through their participation in solving problems, creating, criticizing, and integrating problems with peers and teachers in the classroom. Bergmann and Sams (2012) believe that the core of flipping is to focus on students’ needs, and Bloom Taxonomy provides a framework for judging whether it is flipped teaching: courses centered on past lectures usually focus on the lower level of Bloom Taxonomy, such as the cognition and understanding of basic knowledge. Teachers with flipped teaching will focus more on the high-level learning results of Bloom’s taxonomy in the classroom, such as analysis, judgment, and creation.

Sáiz Manzanares et al. (2017) analyze the effect of blend learning on students’ learning outcomes. The results showed that different learning patterns can predict student learning outcomes. Further, Yin and Yuan (2021) examined the learning performance in a blended learning environment in China and the factors of perceived precision teaching, self-efficacy, learning motivation, and social presence were examined. The results indicated that all the predictors showed significant effect on learning performance, of which self-efficacy is one of the most important factors in predicting learning performance. In addition, Yokoyama and Miwa (2021) examined the effects of self- and peer-assessment on the growth of learning goal orientation. Results from the experiment showed that peer-assessment is effective in enhancing the growth of learning goal orientation.

The above discussion revealed that studies on flipped teaching are varied. However, most of the studies are focusing on language learning. For example, Lee and Wallace (2018) provided empirical evidence about whether flipped learning can promote students’ English learning. Andujar et al. (2020) examine the effect of integrating the flipped teaching and the usage of mobile devices in language learning. Further, Amiryousefi (2019) investigated the effects of flipped learning on EFL (English as a foreign language) learners’ engagement.

This study refers to Abeysekera and Dawson (2015) to explore flipped teaching with the following three characteristics: (1) process-oriented and inquiry-based learning, (2) peer-based team learning, and (3) peer interaction and learning.

Learning Satisfaction

Learning satisfaction has always been a very important research indicator in education-related research. According to a study by Ko and Chung (2014) , there is a significant positive correlation between student learning satisfaction and academic performance. A report on innovative teaching also pointed out that students’ learning satisfaction directly affects their academic performance; thus, it is also one of the main items used to measure or predict learning effectiveness ( Lee, 2011 ).

Chang and Chang (2012) defined learning satisfaction as the degree of happiness that students experience after learning activities. Learning satisfaction exists in the balance between personal expectations and self-realization. When the results of self-realization are equal to or higher than personal expectations, learning satisfaction can be improved; however, when the results of self-realization are not as good as personal expectations, one cannot obtain a sense of achievement in learning ( Martin, 1988 ).

Many factors can affect students’ learning satisfaction. In a study on learning satisfaction among adults receiving computer-related skills teaching, divided learning satisfaction into five items: teacher’s teaching, classroom materials, learning outcomes, interpersonal relationships, and learning environment. In a survey of learning satisfaction among college students, the questionnaire was divided into five items: learning environment, academic performance, administrative services, interpersonal relationships, and attitudes toward teachers and administrators ( Starr, 1971 ). Corts et al. (2000) used five environmental factors to study how student satisfaction was affected. The results of the study show that employability development and curriculum planning have the deepest impact on student satisfaction. Teven and Mccroskey (1997) also prove that teachers’ attention toward students’ learning conditions and their interactions with students contribute to improving students’ learning satisfaction.

Based on the above views of scholars, although the factors affecting learning satisfaction have different research results and opinions because of the research focus of scholars, in fact, the external factors that affect students’ learning satisfaction are mainly constituted by teachers’ teaching methods, arrangement of learning activities, curriculum content planning, classroom teaching materials, learning outcomes, employment skills training, interpersonal interactions with peers and teacher interactions, and other factors.

Learning Self-Efficacy Theory

The self-efficacy theory was first proposed by Bandura in 1977, and Bandura defined it as the degree to which people believe they can accomplish tasks and achieve goals ( Bandura, 2010 ). The influencing factors of self-efficacy mainly come from the following four types: (1) through the successful experience of learning to build a stronger self-efficacy; (2) by seeing people similar to themselves who have worked consistently to achieve success and thus believing that they have similar abilities to be successful; (3) through the verbal encouragement of others, which makes people believe that they have the relevant abilities needed to complete the task, and they are willing to try to improve their self-efficacy; and (4) physiological conditions, negative emotions, and unhealthy physical conditions will lead to low self-efficacy ( Bandura, 2010 ).

Because self-efficacy affects people’s feelings, thinking, and behaviors, it has been widely studied and applied in many fields after it was proposed, which include addiction problems ( Bandura, 1995 ), smoking behavior ( Garcia et al., 1990 ), and athletic performance ( Barling and Abel, 1983 ). In education research, the value of self-efficacy has drawn increasingly more attention ( Pajares, 1997 ). In education, research on self-efficacy focuses on the following three aspects: (1) the relationship between self-efficacy and university majors and career choices ( Lent and Hackett, 1987 ); (2) teachers’ self-efficacy beliefs, teaching practices, and student academic performance ( Ashton and Webb, 1986 ); and (3) the relationship between students’ learning self-efficacy beliefs and academic achievement ( Bartimote-Aufflick et al., 2016 ).

In the research on the relationship between students’ learning self-efficacy and academic performance, students with high learning self-efficacy treat it as a challenge when they encounter difficulties in learning. Such students set challenging goals and continue to work hard. When faced with failure, they attribute the failure to insufficient effort or insufficient knowledge and skills, and they are more willing to keep working hard. On the contrary, students with low self-efficacy choose to escape when faced with difficulties and do not ask for learning goals. They usually give up easily when faced with problems, because they regard insufficient learning self-efficacy as insufficient ability ( Bandura, 2010 ).

According to Bandura’s narrative, it is reasonable to infer that learning self-efficacy has a positive effect on learning effectiveness. In many studies, learning self-efficacy has also been proven to be an important indicator used to predict academic performance ( Elias and Loomis, 2002 ). In addition, because learning outcomes affect learning satisfaction ( Starr, 1971 ), Aitken (1982) also pointed out that grade point average (GPA) has a positive effect on learning satisfaction. After reviewing the literature related to both learning self-efficacy and learning satisfaction, the following hypothesis was proposed:

H1 : Learning self-efficacy will positively influence learning satisfaction.

Need for Cognition

Need for cognition is a personality trait. Cohen et al. (1955) first defined this concept as “a need for individuals to organize their experience meaningfully.” Cacioppo and Petty (1982) modified this viewpoint, thinking that cognitive needs reflect people’s enthusiasm for activities related to cognitive thinking types.

People with low cognitive needs do not like cognitive tasks when dealing with complex problems and tend to rely on others or even expert opinions ( Petty et al., 1981 ). People with high cognitive needs are relatively more willing to devote themselves to thinking tasks or work and are more likely to use systematic thinking to process information. Such people are described as having a high degree of intrinsic motivation, aspiration, and curiosity, so they actively search for information ( Olson et al., 1984 ). As for causes of individual differences in cognitive needs in various social environments, the main reason is intrinsic motivation. This individual difference is stable for a while and not easy to change ( Cacioppo et al., 1996 ).

Most contemporary research involving the cognitive needs theory is based on the discourse of Cacioppo and Petty (1982) . Research involving cognitive needs includes social cognitive psychology, medicine ( Haugtvedt et al., 1992 ), and online consumer behavior ( Lin et al., 2011 ). In the literature related to education, Sadowski and Gülgös (1996) studied how cognitive needs affect academic performance, and the results prove that students with high cognitive needs achieve more academic achievements than those with low cognitive needs, because the former can deal with information more effectively than the latter. Dole and Sinatra (1998) also put forward similar research viewpoints, because students with high cognitive needs also have a higher level of performance in speculation and problem-solving during the learning process; on the contrary, students with lower cognitive needs have a lower level of performance. In a study that discussed the relationship between cognitive needs and the ability to solving complex problems ( Nair and Ramnarayan, 2000 ), it was pointed out that current cognitive needs have a significant positive correlation with solving complex problems, because people with high cognitive needs collect information and make multifaceted decisions about problems; they are more likely to succeed in solving problems.

According to the above discussion about cognitive needs in education literature, we understand that cognitive needs have a significant positive correlation with learning effectiveness. In a study by Elias and Loomis (2002) , the correlation between cognitive needs, learning self-efficacy, and learning effectiveness was verified. It has been proven that cognitive needs and learning self-efficacy are important predictors of GPA ( Strobel et al., 2019 ). Besides, it was also found that the relationship between cognitive needs and GPA was affected by the mediation of learning self-efficacy. Thus, the following hypotheses were proposed:

H2 : Need for cognition will positively influence learning satisfaction.

H3 : Need for cognition will positively influence learning self-efficacy.

Level of Collaborative Learning

Collaborative learning is defined as when students achieve a common learning goal, they complete it in a group and are responsible for each other’s learning ( Gokhale, 1995 ). It is worth noting the difference between “cooperative learning” and “collaborative learning.” Cooperative learning refers to a model in which a learning task is divided into subtasks that can be solved independently by partners at the beginning. Collaborative learning is solving a problem together in an asynchronous and interactive way. The difference between the two is that collaborative learning emphasizes the discussion in the process of participating in tasks and believes that cognition must be adjusted through communication between students ( Curtis and Lawson, 2001 ). Verdejo (1996) emphasizes collaborative learning based on dialogue.

Gokhale (1995) pointed out that active exchange of ideas within the group will not only increase students’ interest but also promote critical thinking. Studies have shown that compared to individual learning, collaborative learning provides students the opportunity to discuss and have a higher level of thinking, and information can also be memorized for longer.

According to the observation of Tuckman and Jensen (1977) , it is pointed out that the interpersonal relationship between group members in collaborative learning will generally go through the following four stages: (1) Formation stage: a transitional period when group members are not familiar with each other; (2) Conflict stage: the transition period in the growth of the group, when group members adapt to each other and run-in; (3) Cohesion stage: If the conflict is handled properly, a balance that is acceptable to the members of a group is sought, gradually forming a consensus, and the cohesion of the group will increase day by day; and (4) Execution stage: team members will focus on the completion of the task and the achievement of the goal. Members depend more on each other, and each person’s role positioning will be more productive.

In the collaborative learning environment, regardless of the level of learning achievement, students generally perform better than their peers who study alone ( Aitken, 1982 ), and in the process of collaborative learning, students’ communication with each other is also considered helpful ( Bruffee, 1982 ). According to the current research on collaborative learning, it is clearly pointed out that collaborative learning can improve learning more effectively ( Hertz-Lazarowitz et al., 2013 ) and reinforce students’ satisfaction with the entire learning process ( Bligh, 1998 ; Ocker, 2001 ). Thus, the following hypothesis was proposed:

H4 : Collaborative learning will positively influence learning satisfaction.

Research Method

Research design.

The study applied both quantitative and qualitative research methods to examine the factors in flipped teaching outcome. In the first stage, a survey was conducted and questionnaires were distributed to students who experienced the flipped learning method. The objective of the survey is to examine predicting factors of learning satisfaction. The study uses need for cognition, learning self-efficacy, and collaborative learning as the predictors that affect the satisfaction of flipped teaching. In the second stage, deep interviews were conducted to understand the collaborative learning process as it is one of the most important mechanisms in flipped learning. The objective of the second stage is to explore the learners’ collaborative learning process in terms of team formation, discussion atmosphere, discussion efficiency, decision-making mode, cooperation mode, and cross-domain learning. In the second stage, semi-structured in-depth interviews were conducted. The interview comprises open questions, starting with the interviewee’s personal background, including questions about name, gender, school, department, grade, and major courses, and then cutting into the core questions of the research gradually. The two-staged research design is depicted in Figure 1 .

Figure 1 . Two-staged research design.

Data Collection

All the participants in this study (including stage 1 and stage 2) were recruited from six courses which were given as flipped teaching methods. The students were either asked to fill out the questionnaire for qualitative research or invited as an interviewee for qualitative research in this study.

This study refers to the flipped teaching model proposed by Abeysekera and Dawson (2015) . The flipped teaching curriculum must have the following three elements: (1) process-oriented inquiry learning, (2) team-based learning, and (3) peer learning. According to this standard, a total of six courses have been selected as the experimental situation, which was described in Table 1 .

Table 1 . The description of courses selected in the study.

The sample of survey and interview are students from the same pool (the six courses listed in Table 1 ). The objective is to examine the factors of learning satisfaction from qualitative study. At the same time, students from the same courses were invited to participate the interview in order to illustrate in more detail about the collaborative learning process in flipped classroom.

The data in this study were collected in two ways. First, students who take the courses illustrated in Table 1 were invited to fill out the questionnaire at the end of the semester. The questionnaires were distributed under the permission of the instructors, and a total of 171 valid samples were returned.

Second, the participants in qualitative study were invited from the six courses in Table 1 . Student was randomly selected to represent each of the top 25% and bottom 25% of the course scores. A total of 12 students from the six courses participated in this research interview. To protect the privacy of interviewees, the student names on the recording form are presented anonymously.

Research Framework

The research framework of this study is depicted in Figure 2 , in which two independent variables (collaborative learning and need for cognition), one mediator (learning self-efficacy) and one dependent variable (learning satisfaction), were included.

Figure 2 . Research framework.

Research Analysis and Results

Quantitative results.

SMART PLS (partial least square) software was used for data analysis, and structural equation model (SEM) was applied. Structural equation model is composed of two parts: measurement model and structural model. The measurement model is used to observe the relationship between potential variables; structural model is used to measure the relationship between variables and potential variables. This study applied confirmatory factors in the measurement model for theory verification and applied path analysis in the structural model to explore the causal relationship between variables.

Demographics

In terms of the gender distribution of the participants (as shown in Table 2 ), the proportion of males was 43.86% of the total subjects, and the proportion of females was 56.14%. In terms of grade distribution, 28.65% of juniors formed the group with the highest distribution, followed by 27.49% of seniors (and above), 26.90% of the first year of graduate school, 7.01% for both the freshman and sophomores, and lastly, 2.92% for the second year of graduate school (and above); as for the distribution of the colleges, the College of Management had the most students, accounting for 53.80% of the total, followed by 14.04% in the College of Electrical Engineering and Computer Science, 12.87% in the College of Agricultural and Natural Resources, 9.94% in the College of Liberal Arts, 4.09% in the College of Science, 2.34% in the College of Engineering, 1.75% in the College of Law and Politics, and 1.17% in the College of Life Sciences.

Table 2 . Demographics ( N =171).

Since the students at the College of Management were set as the largest number of participants in this study, the demographic variables were also affected by the composition of the grade and the college of the testing class. First, gender was the most influential part, as most of the students in the College of Management were female, which led to the reason that most of the subjects were female.

In addition, in terms of grades, it is noteworthy that most departments and colleges generally require courses with a higher level of implementation, and they are generally offered in the upper grades of the university department. This also explains why the distribution of the test subjects was mainly junior and senior students and the first grade of graduates.

Reliability and Validity Analysis

Validity refers to the theoretical extent to which the questionnaire can measure. The commonly applied method to test the validity in structural equation model is the confirmatory factor analysis in the measurement model. In the same factor dimension, if the factor load of each topic is larger, it means the degree of convergence is greater. Usually, it must be greater than 0.7, and the average variance extracted (Schreurs and Dumbraveanu) must be greater than 0.5. Based on these data as the test standard, after running the PLS statistical analysis, the items that did not meet the factor load were deleted: The first 18 questions about cognitive needs retained the first 1, 8, 10, 11, 12, 14, and 15 questions; 10 questions about learning self-efficacy were reserved for Questions 3, 4, 7, 8, 9, and 10; for the level of collaborative learning, except for Question 6, the remaining 6 questions were reserved; for learning satisfaction, the questions were reserved except for Question 6. The AVE of the retained items after organizing all the dimensions was greater than 0.5.

Reliability represents the stability of the subject’s answer. The most applied verification method is Cronbach’s alpha. When the Cronbach’s alpha coefficient is greater than 0.7, the question items of the scale which the respondents fill in are consistent. The Cronbach’s alpha values of the above items that passed the validity test were all greater than 0.7. The validity and reliability analysis results are summarized in Table 3 .

Table 3 . Validity and reliability test results.

Path Analysis

In the structural model, the results were obtained by applying path analysis (as shown in Figure 3 ). In the structural equation model with cognitive needs, learning self-efficacy, and collaborative learning as the independent variables and learning satisfaction as the dependent variable, the adjusted R 2 is 0.594; the model has a certain reference level. In the structural equation model with cognitive needs as the independent variable and learning self-efficacy as the dependent variable, the adjusted R 2 is 0.354, and the explanatory power of the model reached a significant level.

Figure 3 . Path analysis results. *** p <0.05

In addition, the β coefficient of the path of learning self-efficacy to learning satisfaction is 0.104, and the p value is 0.117; the β coefficient of cognitive needs to learning satisfaction is 0.289, and the p value is 0.000; the β value of collaborative learning degree is 0.536, and the p value is 0.000. In addition, the β coefficient of the path of cognitive needs to learning self-efficacy is 0.598, and the p value is 0.000.

Qualitative Research

Design of interview outline.

Due to the finding in the quantitative research results that the level of collaborative learning has a very critical impact on learning satisfaction, more in-depth research will be conducted on collaborative learning after the quantitative research.

Since the interview was conducted in May, there had been a period of time since the end of class last semester. Therefore, in addition to referring to the items in the questionnaire that involve collaborative learning ( Kitchen and Mcdougall, 1999 ; Driver, 2002 ; So and Brush, 2008 ), the questions of the interview outline were also designed to be combined with Tuckman’s five stages of group development ( Tuckman and Jensen, 1977 ), and the questions were presented in a chronological manner to prevent the interviewee’s course experience from being distorted by time factors, as shown in Table 4 .

Table 4 . Interview outline.

Results of the Interview

The interview time ended within 15min on average, and sometimes, the order and direction of questions were adjusted according to the interviewee’s responses. During the interview process, questions outside the interview outline were also asked to get more in-depth details. The interviews were recorded using audio recording. Before recording, relevant explanations on research ethics and privacy were be provided to inquire about the interviewee’s willingness to record.

After the interview, the interview records were sorted into verbatim drafts based on the recording content, and after the interview records were converted into verbatim manuscripts, the words were segmented for each verbatim manuscript. The meaningless auxiliary words were removed, and the units with clear semantic meaning and readability were retained. After that, the meaningful units were coded. There were six codes in total; for example (consult the teaching assistant, or check it online to see if you have found a solution), the code from left to right is the interviewee’s number, interview question number, and verbatim serial number.

Interviewees codes 01 and 02 are for Marketing Management; 03 and 04 for Knowledge Creation and RandD Management; 05 and 06 for Entrepreneurial Management; 07 and 08 for Fundraising Platforms; 09 and 10 for Introduction to Computer and Internet Security; and 11 and 12 for the Reading Industry and Cultural Communication. When the interviewee’s number is an odd number, it means that the respondent was sampled from the high subgroup (top 25%); when a respondent number is an even number, it means that the respondent was sampled from the low subgroup (bottom 25%).

The two codes of the interview questions were based on the interviewee’s response to the question number of the interview at that moment and are noted with 01~07. The two codes at the end of the number are the serial numbers of a single independent verbatim manuscript that were marked as meaningful sentence units.

Finally, after sorting out all the coded sentences in each verbatim manuscript, the study performed thematic classification to obtain the classification result. Six topics about collaborative learning, including team formation, discussion atmosphere, discussion efficiency, decision-making mode, cooperation mode, and cross-domain learning, were obtained. The following section will discuss in detail each topic.

Formation of the Team

When the curriculum design is carried out in a flipped way, if there is not enough planning before the course for students to understand each other’s expertise and motivation to execute the project, the team composition tends to be random and members tend to find people who they already know to work with. This leads to the cohesion of team members to assume certain risks when the team executes the project, which indirectly affects the degree of classroom engagement; for example, “We started with a very fragmented consensus, because we all have different levels of expectations or understanding of this team.”

However, before the course starts officially, it is necessary to arrange some courses that can help students understand each other’s expertise and motivation. This will reduce unnecessary risks and help students find the right group before the course and is a better way to build team consensus when motivation is the same. Especially, when the project of the course involves interdisciplinary learning, it is also beneficial for students to combine their respective expertise for collaborative learning. For example, “there are some occupations in the publishing industry, such as editor-in-chief, editor-in-charge, and editor-in-art. Then, because the teacher has made this part of the assignment, the team members have a clear sense of their responsibilities and position. I think this is very helpful for grouping.”

It is worth noting that the consensus within the team will change with the development of the team, and the team goals may be more focused due to the organizational changes in the team. “After we started to do something, I think things were more on track.” When members were more willing to participate in the learning task of the course, members would also be more likely to focus on the overall goal of the team.

Discussion Atmosphere

The discussion process of collaborative learning may also lead to conflicts, which will affect the degree of engagement of the members. When there is a conflict in the discussion process, the group with a higher willingness to invest tends to face it actively and is more willing to take the initiative to put forward its own opinions and communicate with the members of the group: “We were livelier when we had meetings. We had a lot of trash-talking, so everyone … everyone felt that there is no sense of distance. Thus, we just kept throwing out ideas like this.” The group with a lower level of involvement was more inclined to avoid expressing their true ideas: “The discussions in our group are not particularly enthusiastic. It is more like ‘business is business’, that is, finish what you are responsible for and then hand over the results. Then nobody will raise too many objections; however, I do not think this is a good thing.”

One thing that can be noticed from here is that when team members encounter conflicts during a discussion, that does not deteriorate team relationships. When team members are more willing to communicate, moderate conflicts are often a boost for the team to generate innovative ideas.

Discussion Efficiency

Discussions between groups can be divided into three: (1) online plus in-person discussion, (2) online discussion, and (3) in-person discussion. From the feedback of the interviewees, it can be found that the efficiency of the group that only has online discussions is low. Online discussions often rely on social software, such as Line to communicate in asynchronous text. This mode of discussion may be inefficient because of the time gap in information: “It may be that everyone discusses a question, but the time taken by each person to answer it is different, and sometimes, it could be a long time. That is, the group must wait for everyone to give feedback and may have to wait for a long time.” In addition, this type of discussion often leads to team members only focusing on completing their assigned learning tasks and not communicating ideas.

Compared with the purely online text discussion, the physical discussion can encourage students to exchange more ideas, but there are also problems of inefficiency, and the underlying cause is often too many ideas among members. Opinions cause the discussion topics to lose focus, which is different from online discussions because of delays in the transmission of information; for example, “I think our group is mostly discussing… There are real discussions, where everyone would throw ideas, but we tend to have no conclusion.”

The online plus physical discussion approach has the characteristic of balancing the lack of the above two approaches. Face-to-face discussions ensure that participants exchange ideas, while implementation details can be tracked through an online communication software. It must be noted that compared to the form of pure online communication, this method focuses on communication software to track implementation details, rather than communicating ideas: “You can complete your large framework in the classroom, and the rest are the details. We have created a similar group for the details. If you have a problem or if you have any ideas, you can just type them and drop them in the group first.”

Decision-Making Mode

When team members face making important decisions, they can do so in two ways: (1) by reaching a consensus within the team members or (2) by seeking external resources. Consensus reached by group members can be further divided into two types: group members with clear roles and decentralization. Based on the results of this research interview, the group to which the sampled interviewees belonged generally tended to make important decisions in a decentralized manner. Compared to the group with a clear leader making decisions, it seems impossible to clearly point out the advantages and disadvantages, but from the results of this study, it is found that the decision-making model of group members without clear roles allowed each group member’s opinions to be fully heard and ensured that each member could participate in collaborative learning and jointly take the risk of decision-making; for example, “We will first listen to everyone’s opinions, and then, if there are different opinions, we will, for example, have different people come up with different solutions, after which we will analyse each solution individually and discuss the current situation, see the advantages and disadvantages of each plan, and, finally, see what solution will be most suitable for us.”

When there is no consensus within the group or when team members’ knowledge is not yet sufficient to make decisions, students will seek external resources such as classroom teachers. In the context of flipped teaching, when students’ abilities are not enough to cope with the problem, the teacher’s timely initiative to provide assistance is a link that must be paid attention to in flipped teaching design, which helps students to have a clearer direction when analyzing problems; for example, “When our team members just could not make a decision, we would ask the opinions of others, such as the teaching assistants or teachers, and then reach a unified opinion.”

Cooperation Mode

The results of this part of the text analysis are directly related to the formation of the first part of the team. In the initial stage of team formation, if students fully understand the specific expertise of the project that needs to be implemented in the course tasks, they will look for team members with relevant expertise when forming the team. The division of roles will also be more efficient in collaborative learning; for example, “Those who have a professional background are really good at certain aspects of tasks, that is, when they are good in the field, they will be relatively helpful, and they can do better than someone who spent the same amount of time.”

In contrast, if the division of labor cannot rely on the distribution of expertise among students, it will lead to a decline in efficiency: “Sometimes, I feel the function was allocated a little bit. The function was not evenly distributed, and then it did not show us what the team should be doing clearly.”

Interdisciplinary Learning

We can find from the results of text analysis that when the learning task of collaborative learning needs to be executed across domains, it is helpful to make use of the students’ own expertise, thereby enhancing students’ creativity, empathy, and even promoting mutual learning.

This result is directly related to the cooperation model in point (5). When the project is based on the division of expertise among group members so that the students’ own expertise or professional knowledge can be used, it will help improve the level of collaborative learning: “In fact, he might have learned this expertise in the club or in a school department, but because of this course and that we got together, we all have something to offer to the group. I think this is very important.”

In this learning process, students with different backgrounds of expertise can learn from each other and even improve their ability to innovate: “If we have different backgrounds, we may have different ideas. We may see different levels and different aspects. After discussion or communication, I may be able to understand why the other people would think a certain way, I can understand more things, or why I have never thought about it from his perspective. I need these things instead.”

Of course, in the process of communication, students will also improve their empathy and the ability to step into someone’s shoes, because they see the differences between group members: “If you work with people with varied information backgrounds, the points of concern will be different.”

It is worth noting that the above-mentioned positive responses can be observed regardless of the student’s learning effectiveness.

Research Findings

From the survey-based regression model, it is found that the cognitive needs of students and the degree of collaborative learning are directly related to the learning satisfaction of flipped teaching. This is in line with the focus of this study: Can specific personality traits be used to predict individual differences in students’ responses to flipped teaching? The results of this study prove that students with high cognitive needs have a relatively high degree of investment in the learning context of flipped teaching. The explanation for this phenomenon is the fact that flipped teaching in curriculum design requires students to show higher-order learning skills, such as analysis, judgment, and creativity in Bloom Taxonomy. Cognitive needs are directly related to these abilities. The research results of Nair and Ramnarayan (2000) prove that people with higher cognitive needs are more likely to succeed in solving problems. While Day et al. (2007) explored the association between cognitive needs and learning complex skills, they also confirmed that groups with high cognitive needs are helpful for learning complex skills.

In addition, the peer learning elements that flipped teaching emphasizes ( Abeysekera and Dawson, 2015 ) are described as follows. The level of student engagement in the degree of collaborative learning also has a significant impact on the learning satisfaction of flipped teaching. In the regression model, it can be found that the β value of 0.536 for the level of collaborative learning is much higher than the 0.289 for cognitive needs. A phenomenon can be found here that the key factor affecting students’ investment in flipped teaching is that the needs of groups are greater than those of individuals. In fact, this result is not difficult to understand. When flipped teaching requires a large number of team-based methods, the interaction between the subjects and their peers in the classroom will naturally affect the degree of students’ involvement in the classroom. A study on the impact of teamwork on individual engagement and performance in the workplace environment also puts forward a similar viewpoint ( Stashevsky and Levy, 2014 ), which argued that the quality of teamwork is what affects an individual’s willingness to engage in work.

In the relationship between cognitive needs and learning self-efficacy, the β value is 0.598 and has a significant impact, which proves that students with high cognitive needs will also have a higher level of learning self-efficacy, in other words, higher self-confidence, which is in line with our common sense judgment: When faced with a problem that requires time to think, students who like to think often have more confidence in solving the problem than students who are unwilling to think.

It should be noted that the results obtained in this study are mainly focused on learning satisfaction rather than learning effectiveness; thus, the impact of flipped teaching on learning effectiveness is not included in the discussion.

Thus, the qualitative study based on deep interview is worth to demonstrate the collaborative learning process of students participating the flipped learning. The results showed six important issues in facilitating the collaborative learning: team formation, discussion atmosphere, discussion efficiency, decision-making mode, cooperation mode, and cross-domain learning. (1) Team formation is the first important step of collaborative learning. The ice breaking activities helping students understand each other are an important mechanism before the course formally begin. (2) The building of discussion atmosphere is the second step facilitating collaborative learning, especially when there is a conflict in the discussion process. When team members are more willing to communicate, moderate conflicts are often a boost for the team to generate innovative ideas. (3) The discussion efficiency can be enhanced by using in-person discussion, and online discussion is discouraged for effective group discussion. (4) Team members showed two ways to achieve consensus—centralized and decentralized decision-making mode. (5) As team members have different background, the division of roles will be more efficient in collaborative learning. (6) If the team members corporate with each other by respecting the expertise, the team work efficiency can be improved.

Prior studies on flipped learning mainly focused on language teaching ( Lee and Wallace, 2018 ; Andujar et al., 2020 ). This study is one of the limited studies that addressed the flipped learning in a variety of different courses, covering a wider range of domain and student background. Further, Tomas et al. (2019 ) explore how a flipped classroom supported students’ engagement and learning. The survey results suggested that the mechanism to enhance learning should be designed according to students’ learning needs and their readiness for a flipped learning approach. The results correspond to current study that the collaborative learning, atmosphere, and learners’ personality are important facilitators for learning outcome in flipped classroom.

To summarize, this study examined the predicting factors of learning satisfaction in flipped learning by using questionnaire survey, and the result suggested that collaborative learning if one of the most important predictors of learning outcome. Thus, a follow-up deep interview was further conducted to explore the collaborative learning process in flipped learning and six different important factors in collaborative learning were explored.

Research Contribution

The results of this study prove that the level of collaborative learning is important to the engagement of students in the classroom. When implementing flipped teaching, apart from paying attention to students’ individual differences, it is also necessary to think about how to build a better team learning environment. This provides a direction of thought for educators who want to promote flipped teaching in the future.

In addition, according to the results of interview, when the course is carried out in groups, the teacher can arrange engagement motivation that can promote students’ understanding of each other’s expertise and course tasks. This enables students to find suitable groups before the course to facilitate the subsequent integration of their respective expertise. It also helps students build consensus within the team, and it is easier to build consensus within the team as the course is in progress. It can prevent students who do not know each other at all during the course from forming a group of students who may be inconsistent with their own goals for the course tasks. Besides, when the group members are faced with conflicts of views, if they can maintain good communication, it will help improve the students’ participation in the classroom; on the contrary, in the face of conflict, if there is no timely communication and mutual discussion among team members, the degree of students’ engagement in the classroom will be affected to a certain extent. When the students in the team do not have enough professional knowledge to reach a consensus in the face of conflict decision-making, it is very important to provide appropriate resource assistance in the classroom, such as teachers and teaching assistants. When the group is composed of members with diverse backgrounds, it will help enhance creativity and empathy and also enable students to contribute their knowledge and learn from each other in the process.

Research Limitations and Future Research Directions

The first research limitation of this study is the small sample size caused by inviting only the participants from one of the six courses that meet the criteria of flipped teaching and get the permission of the instructors. Although the sample size is small, both qualitative and quantitative studies were conducted to answer the research questions deeply and broadly. Further, the courses considered in this study are restricted mainly in college of management and thus can limit the domains to be applied based on the current findings. As courses given in different domains (i.e., management, medical, science, and liberal) may have very different characteristics, the flipped teaching methods will also be different. Thus, the future studies can be suggested to include more courses in different domains in order to compare the facilitating factors of flipped learning satisfaction and outcome.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

FF-C: study conception and design. P-CS: data collection. P-CS and C-SW: analysis and interpretation of results. FF-C and C-SW: draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.

Ministry of Science and Technology in Taiwan for financially supporting this research under Contract No. MOST-110-2410-H-029-023.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: flipped education, need for cognition, learning self-efficacy, collaborative learning, learning satisfaction

Citation: Cheng F-F, Wu C-S and Su P-C (2021) The Impact of Collaborative Learning and Personality on Satisfaction in Innovative Teaching Context. Front. Psychol . 12:713497. doi: 10.3389/fpsyg.2021.713497

Received: 23 May 2021; Accepted: 23 August 2021; Published: 29 September 2021.

Reviewed by:

Copyright © 2021 Cheng, Wu and Su. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chin-Shan Wu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Online Collaborative Learning in Higher Education: A Review of the Literature

As an instructor in a fully online program, I often use group work as a means to increase engagement and facilitate a connection in the online classroom. In some classes, I ask students to work in groups on individual assignments, but for the purpose of giving and receiving feedback on their respective projects. For example, in a course on Nutrition Education Methods, students work to develop individual lessons that they ultimately deliver at the end of the term. In this case, peer feedback is used to strengthen their work. In other classes, I ask students to work together in groups where they all contribute to a larger, shared project that they submit at the end of the term. In a course on Health Communication, for example, students work collaboratively to develop and implement a social marketing campaign that addresses a health-related issue of their choosing.

In course evaluations, the group assignments established for giving and receiving peer feedback are generally well-received and students note their appreciation for their groups’ remarks. In the second example, student evaluations about their experience are often mixed. Some report a positive group experience, while others are disappointed with the final outcome.

The ability to work collaboratively with a team is a skill that serves students well beyond their college years. A  recent article  on LinkedIn Learning (Pace, 2020) outlines the “soft” skills that companies are seeking in prospective employees in 2020. These skills include creativity, persuasion, collaboration, adaptability, and emotional intelligence – all skills that “demonstrate how we work with others and bring new ideas to the table” (Pace, 2020, para. 2). As an instructor, I see the larger benefits of collaborative learning, but recognize how these assignments translate in the online classroom isn’t always successful.

In this review of the literature, my aim is to share the results of my research on collaborative learning and its applications in the online environment in higher education, as well as the circumstances that make collaborative learning a positive experience for students and teachers alike.

What is Collaborative Learning?

The word collaboration “suggests a way of dealing with people which respects and highlights individual group members’ abilities and contributions. There is a sharing of authority and acceptance of responsibility among group members for the groups’ actions” (Laal & Ghodsi, 2012, p. 486). Collaborative learning requires that learners work together to make connections and uncover new ways of understanding concepts (Laal & Laal, 2012 as cited by Falcione et al., 2019). Falcione et al. (2019) add to this definition by explaining that collaborative learning is a way for students to intertwine their independent work in order to achieve a shared goal. The results of these efforts are a “product or a learning experience that is more than the summation of individual contributions” (Falcione et al., 2019, p. 1).

The foundation of collaborative learning is the idea that learning with others is better than learning alone (Nokes-Malach et al., 2015). In fact, the primary goal of team-centered, collaborative environments is to apply the unique backgrounds and skills that individuals bring to a group and accomplish something together that they may otherwise be unable to accomplish individually (Roberts, 2004).

Online learning naturally lends itself to student-centered instructional strategies and assessments, and collaborative learning most certainly fits this category (Muller et al., 2019). Given the physical distance that separates online students, collaborative learning efforts may also help students connect in an effort to dissolve any feelings of isolation they may be experiencing (Writers, 2018).

Harasim (2012) as cited by Bates (2015), offers the following definition of Online Collaborative Learning (OCL):

Online collaborative learning theory provides a model of learning in which students are encouraged and supported to work together to create knowledge: to invent, to explore ways to innovate, and, by so doing, to seek the conceptual knowledge needed to solve problems rather than recite what they think is the right answer (Harasim, 2012 as cited by Bates, 2015, para 1).

The image shown below depicts the core principles of Online Collaborative Learning and how these principles are operationalized through online discussions. Discussion forums often serve as the backbone for learning in online environments. Bates (2015) argues that online discussion forums are not meant to supplement course content (typically delivered through lectures and textbooks), but should be the central means for content delivery. Here, students identify readings and resources  to support the discussion  as opposed to allowing the readings and resources to be the driver. It is through this discourse that students are able to generate and organize ideas and ultimately achieve “intellectual convergence” by synthesizing the ideas presented (Bates, 2015). Because the discussion happens asynchronously, students have time to ruminate over the ideas presented and respond in a more thoughtful manner (Roberts, 2004).

(Bates, 2015)

Another important element of the Online Collaborative Learning model depicted above is the role of the teacher. Here, the teacher serves as a facilitator of the discussion in an effort to move students through the process of generating, organizing, and synthesizing ideas (Bates, 2015). The concept of “teacher” as “facilitator” is a hallmark of student-centered, online learning.

Related Terms and Theories

Collaborative learning is sometimes used interchangeably with the term “cooperative learning” (Writers, 2018). Balkcom (1992) defines  cooperative learning  as an instructional strategy that uses groups made up of diverse learners. Groups are engaged in a variety of activities to enhance their understanding of lesson concepts, and members have a shared responsibility to help one another learn and grow. A key component of cooperative learning is  positive interdependence  ( What is Cooperative Learning , n.d.). Positive interdependence is established when students perceive that the contribution of each group member is essential to the success of the group. Scager et al. (2016) found positive interdependence to be a critical factor in successful collaboration.

While the two concepts share many of the same characteristics, Falcione et al. (2019), argues that cooperative learning is, in fact, different from collaborative learning. The primary factor that differentiates collaborative learning from cooperative learning is the independent work that group members do in order to contribute to the task at hand. This work is done at different times and is often developed alone. However, the individual’s work is later combined with the work of other group members in order to synthesize ideas. The following video expands on this idea and identifies additional factors that differentiate collaborative learning from cooperative learning:

(Wufei87, 2018)

Another related concept evident in the literature is a Community of Inquiry (CoI). In the image below, Garrison, Anderson, and Archer (2000) depict the CoI framework that they argue is integral to the online learning experience in higher education.

(Garrison, Anderson, & Archer, 2000, p. 88)

Here, the educational experience is at the center of the CoI model, and learning takes place through the interaction of three vital components: social presence, cognitive presence, and teaching presence. Social presence represents the idea that individuals within the community are able to interject elements of their personality into the group so that they are seen as “real people” (Garrison, Anderson, & Archer, 2000, p. 89). Cognitive presence is deemed the most important of the three and refers to the ability of learners to “construct meaning through sustained communication” (Garrison, Anderson, & Archer, 2000, p. 89). The authors argue that cognitive presence is critical to developing higher-order thinking skills (which is necessary in postsecondary education). Finally, teaching presence is defined by two key functions: 1) course design and 2) facilitation. Essentially, the goal of teaching presence is to facilitate cognitive presence and social presence within the community (Garrison, Anderson, & Archer, 2000). Bates (2015) concludes that CoI and OCL are more “complementary rather than competing” (section 4.4.3) ideas and are, therefore, not mutually exclusive models for learning.

Online collaborative learning may be classified as a constructivist approach to learning (Bates, 2015). Constructivism is a theory that posits that learners actively construct knowledge as opposed to passively receiving it. This knowledge is further developed through life experiences allowing learners to develop mental models as a way to make sense of new information ( Constructivism , n.d.). The table below outlines the differences between traditional learning and constructivist learning:

(Constructivism, n.d.)

Examples of Collaborative Learning

In the traditional classroom setting, collaborative learning can take on many forms. Problem-based learning, jigsaw activities, think-pair-share, and peer review are just a few common examples (Nokes-Malach et al., 2015). These strategies are defined in more detail below:

Problem-based learning:  In this strategy, students work in groups to collaboratively solve a larger problem. The group work takes place over an extended period of time and often requires some deliverable at the end of the project (Active and Collaborative Learning | University of Maryland—Teaching and Learning Transformation Center, n.d.).

Jigsaw:  This strategy takes a problem or task and divides it into smaller components. Each component is assigned to a group in order to gain a deeper understanding of the topic, who ultimately reports out in an effort to contribute their understanding as a piece of the larger puzzle (Active and Collaborative Learning | University of Maryland—Teaching and Learning Transformation Center, n.d.).

Think-pair-share:  This strategy starts by dividing students into pairs. The instructor then provides students with a discussion prompt or question to consider. Individual learners reflect on the problem independently before sharing their thoughts or ideas with a peer. Once both students have had a chance to discuss, they may share a summary of their discussion with the rest of the group (Active and Collaborative Learning | University of Maryland—Teaching and Learning Transformation Center, n.d.).

Peer review:  This strategy allows students to review one another’s work and provide positive and constructive feedback to facilitate improvement. The strategy teaches students as writers to receive, evaluate, and choose whether or not to incorporate the feedback into their work. As editors, it teaches students to analyze and clearly communicate feedback with their peers. As an instructor, it is critical to provide guidance and structure to best facilitate the process (Active and Collaborative Learning | University of Maryland—Teaching and Learning Transformation Center, n.d.).

All of these strategies can be adapted for the online learning environment, however, online collaboration tools, i.e.  Zoom ,  Google Docs ,  S lack , or  T rello , are often used to facilitate the transition (Writers, 2018). Tarun (2019) defines online collaboration tools as “web-based tools that allow individuals to do things together online like messaging, file sharing, and assessment” (p. 276). Integrating technology tools like these in the classroom fosters “authentic and meaningful learning experiences” (Boundless, 2015, sec 2) and also supports differentiated learning efforts (Boundless, 2015).

A basic search online for “online collaboration tools for education” yielded a variety of sites ranking the top-rated tools for digital collaboration (EDsmart, 2015; TeachThought, 2019). In the 2019 article, the tools ranked covered broad categories like tools for communication, project management, peer review, and game-based learning. Listed below are some examples that the authors highlighted in this post:

Diigo : Diigo is a social bookmarking tool that allows learners to collect, annotate, organize, and share online resources.

Flipgrid : Flipgrid is a tool that allows learners to create and share short videos and can be used for reflections, discussions, or short presentations. Additionally, peers can respond to posts in video form. The “grade book” feature within the tool allows instructors to track and monitor participation.

VideoAnt : VideoAnt is a tool that allows students and teachers to annotate YouTube videos. Here, students can ask questions or add critiques at various spots throughout the video.

Padlet : Padlet is a multimodal group collaboration tool. Here, students can collect videos, articles, or images and post them to a virtual corkboard. Students can also comment on posts in a threaded discussion format.

Appavoo, Sukon, Gokhool, and Gooria (2019) add that tools like WhatsApp, Skype, and Moodle are popular tools for online collaborative learning in higher education. These tools offer learners a way to discuss and share ideas and gain instant feedback. Furthermore, some students report that they prefer to learn on tools like these as they feel more open to discussing any academic-related issues they may be experiencing (Preston, Phillips, Gosper, McNeil, Woo, and Green, 2010, as cited by Appavoo et al., 2019).

Benefits of Collaborative Learning

Scager et al. (2016) note that there are decades of literature that demonstrate the positive effects of collaborative learning on academic success. In one such article, Laal and Ghodsi (2012) compiled and categorized the benefits of collaborative learning found in the literature between 1964-2010. The noted benefits were divided into four overarching categories to include social, psychological, academic, and assessment.

Social : Collaborative learning creates a support system for students as they work through challenges together. The group work also facilitates learning communities while improving student’s understanding of diverse viewpoints and strengthening cooperation.

Psychological : Learner-centered instruction improves self-confidence in the learner and working on problems together can help lessen feelings of anxiety for students. Affectively, collaborative learning efforts may lead to more “positive attitudes towards teachers”.

Academic:  Collaborative learning creates a student-centered approach to learning, fosters higher-order thinking and facilitates problem-solving skills.

Assessment:  Collaborative learning efforts use a multitude of assessment techniques.

Falcione et al. (2019) add that collaborative learning leads to a mastery of course content and the cultivation of interpersonal skills that benefit the student outside of the classroom environment.

In collaborative learning, the metacognitive ability of participants is improved due to the absence of a professor’s help throughout the process; learners must turn to each other, or outside sources, to overcome barriers, encouraging recognition of their own misunderstandings. (Davidson & Major, 2014 as cited by Falcione et al, 2019).

Benefits Specific to Online Collaborative Learning Roberts (2004) describes additional benefits specific to collaborative learning in the online environment. Examples include:

• Quiet students may open up.
• Little off-task behavior.
• The asynchronous nature of discussions fosters deeper responses.
• Students can use technology tools to access additional information.
• Few student disruptions.
• The content of online discussions can be retrieved at a later time.
• Discussions can extend across the term.
• Online learning creates an environment that supports the instructor’s role as facilitator.

Challenges with Collaborative Learning

While there are many documented benefits of collaborative learning, this strategy also comes with its fair share of challenges. One such challenge includes the “cognitive costs of coordinating and collaborating with others” (Nokes-Malach et al., 2015, p. 647). In other words, if an individual member can solve the problem independently, then they are not likely to benefit from collaborative efforts and may even perform worse as a result of trying to coordinate many varied ideas (Nokes-Malach et al, 2015 as cited by Nokes-Malach et al., 2012). This notion also applies to less complex activities where little is gained from group collaboration. Group members benefit when the task is complex, i.e. “high cognitive load”, and parts can be distributed among the group.

Other potential challenges described by Nokes-Malach et al. (2015) include “retrieval strategy disruption” and “production blocking”. The former concept occurs when one person loses their train of thought because they are paying attention to other group members, while the latter refers to the practice of allowing others to finish speaking before attempting to speak. This example can lead to “missed retrieval opportunities”.

A third example includes “social loafing” which describes the phenomenon where one group member may not contribute at the same level because they believe other group members may help “pick up the slack”.

A fourth, and final challenge is of collaborative learning is “fear of evaluation”. Here, students may avoid sharing ideas out of fear of judgment from their group members (Nokes-Malach et al., 2015).

Johnson and Johnson (2009, as cited by Nokes-Malach et al., 2015) propose that the latter two examples may occur when there is a lack of individual accountability or positive interdependence among group members as described earlier in this review.

It’s important to note that there are also drawbacks related more specifically to online collaborative learning efforts. One such drawback involves some of the online collaboration tools used. Tarun (2019) discusses the inadequacies of such tools to include a lack of features that may improve usability as well as the inability to customize some tools to meet classroom, instructor, or school needs.

Appavoo et al. (2019) add that collaborative learning efforts in online courses can be difficult to coordinate for learners, as some are also balancing professional and family-related commitments.

Implications for Future Work

In order to overcome some of the common challenges of collaborative learning and maximize benefits, it is important to adhere to the recommendations that have emerged from the research on collaborative learning efforts (Scager et al., 2016).

The first factor that instructors should keep in mind when implementing collaborative learning efforts is to use a small group size (Scager et al., 2016). Three to five students per group is recommended to maximize efficacy ( Cooperative learning classroom.research , n.d.).

Another factor to consider is group composition. Groups comprised of members with diverse perspectives have been shown to increase learning in group work (Kozhevnikov et al., 2014). It is interesting to note that mixed ability groups tend to benefit lower ability students and may not benefit higher ability students (Webb et al, 2002). What may be even more important when it comes to learning, however, is equal participation among group members regardless of “ability”. When all students participate equally, they are more likely to fully utilize each member’s unique skillsets and contributions (Woolley et al., 2015).

A third factor to consider is the nature of the task itself. For collaborative learning efforts to be most successful, tasks should be both complex and appropriate for the topic at hand. The task should also allow students to create unique work with autonomy and self-regulation (Scager et al., 2016), but within a structure or framework to guide collaborative learning efforts (Appavoo et al., 2019).

Last, but not least, for collaboration to be successful, social interaction is imperative (Volet et al., 2009). The process of discussing, debating, and explaining ideas to one another, as well as building off of others’ ideas helps to facilitate metacognition (Scager et al., 2016).

Gaps and Conclusions

While the concept of collaborative learning certainly isn’t new, collaborative learning in a digital environment is for many teachers and students. With the advances in technology as well as the increase in quantity and quality of digital tools available, there is great potential for the future of online learning. To get to that point, however, it will be important to address some of the gaps in the existing literature.

Research efforts for this review uncovered fewer articles related specifically to online collaborative learning when compared to collaborative learning in the traditional classroom setting. Chang and Hannafin (2015) add that it will be important to consider the unique traits of adult-learners and the impact that online collaboration tools may have on learning for this group.

Tarun (2019) notes that research on educational technology tools most often includes tests of quality, to include “functionality and usability”, but fail to evaluate the effects of integration into the online classroom. In future research, it will be important to consider if and how the technology tools used for collaboration are actually accomplishing what educators believe they are accomplishing.

Active and Collaborative Learning | University of Maryland — Teaching and Learning   Transformation Center . (n.d.). Retrieved April 4, 2020, from https://tltc.umd.edu/active-and-collaborative-learning

Bates, A. W. (Tony). (2015). 4.4 Online collaborative learning. In  Teaching in a Digital Age . Tony Bates Associates Ltd. https://opentextbc.ca/teachinginadigitalage/chapter/6-5-online-collaborative-learning/

Boundless (2015, July 21). Advantages of using technology in the classroom.  Boundless Education . Retrieved from http://oer2go.org/mods/en-boundless/www.boundless.com/education/textbooks/boundless-education- textbook/technology-in-the-classroom-6/edtech-25/advantages-of-using-technology-in-the-classroom-77- 13007/index.html

Chang, Eunice & Hannafin, M. J. (2015). The uses (and misuses) of collaborative distance education technologies: Implications for the debate on transience in technology. Quarterly Review of Distance Education ,  16 (2), 77–92.

Constructivism . (n.d.). Retrieved April 4, 2020, from http://www.buffalo.edu/ubcei/enhance/learning/constructivism.html Cooperative learning classroom.research . (n.d.). Retrieved April 4, 2020, from http://alumni.media.mit.edu/~andyd/mindset/design/clc_rsch.html

EDsmart. (2015, December 29).  50 free online collaboration tools for educators .  https://www.edsmart.org/50-free-online-collaboration-tools-for-educators/

Falcione, S., Campbell, E., McCollum, B., Chamberlain, J., Macias, M., Morsch, L., & Pinder, C. (2019). Emergence of different perspectives of success in collaborative learning.  Canadian Journal for the Scholarship of Teaching and Learning ,  10 (2). https://eric.ed.gov/?id=EJ1227390

Kozhevnikov, M., Evans, C., & Kosslyn, S. M. (2014). Cognitive style as environmentally sensitive individual differences in cognition: A modern synthesis and applications in education, business, and management.  Psychological Science in the Public Interest ,  15 (1), 3–33. https://doi.org/10.1177/1529100614525555

Laal, M., & Ghodsi, S. M. (2012). Benefits of collaborative learning.  Procedia – Social and Behavioral Sciences , 31, 486–490. https://doi.org/10.1016/j.sbspro.2011.12.091

Muller, K., Gradel, K., Forte, M., McCabe, R., Pickett, A. M., Piorkowski, R., Scalzo, K., & Sullivan, R. (n.d.).  Assessing Student Learning in the Online Modality . 32.

Nokes-Malach, T. J., Richey, J. E., & Gadgil, S. (2015). When is it better to learn together? Insights from research on collaborative learning.  Educational Psychology Review ,  27 (4), 645–656. https://doi.org/10.1007/s10648-015-9312-8

Roberts, T. S. (2004).  Online Collaborative Learning: Theory and Practice . Idea Group Inc (IGI).

Scager, K., Boonstra, J., Peeters, T., Vulperhorst, J., & Wiegant, F. (2016). Collaborative learning in higher education: Evoking positive interdependence.  CBE Life Sciences Education ,  15 (4). https://doi.org/10.1187/cbe.16-07-0219

Tarun, I. M. (2019). The Effectiveness of a Customized Online Collaboration Tool for Teaching and Learning.  Journal of Information Technology Education: Research , 18, 275–292.

TeachThought. (2019, June 9).  30 Of the best digital collaboration tools for students .  https://www.teachthought.com/technology/12-tech-tools-for-student-to-student-digital-collaboration/

Volet, S., Summers, M., & Thurman, J. (2009). High-level co-regulation in collaborative learning: How does it emerge and how is it sustained?  Learning and Instruction ,  19 (2), 128–143. https://doi.org/10.1016/j.learninstruc.2008.03.001

What is Cooperative Learning?  (n.d.). Cooperative Learning. Retrieved April 3, 2020, from https://serc.carleton.edu/introgeo/cooperative/whatis.html

Writers, S. (2018, February 14).  Current Trends in Online Education . TheBestSchools.Org. https://thebestschools.org/magazine/current-trends-online-education/

May 7, 2020

Strategies for Online Learning

collaborative learning , cooperative learning

July 29, 2020 at 8:32 pm

This is a great article for the times. How to make sure you get social interaction while operating a digital classroom is so important.

December 14, 2020 at 9:53 am

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• v.15(4); Winter 2016

Collaborative Learning in Higher Education: Evoking Positive Interdependence

Karin scager.

† Department of Social Sciences, Utrecht University, 3508 TC Utrecht, The Netherlands

Johannes Boonstra

‡ Department of Biology, Utrecht University, 3584 CH Utrecht, The Netherlands

Ton Peeters

Jonne vulperhorst, fred wiegant.

This study focuses on factors increasing the effectiveness of collaborative learning. Results show that challenging, open, and complex group tasks that required the students to create something new and original evoked effective collaboration.

Collaborative learning is a widely used instructional method, but the learning potential of this instructional method is often underused in practice. Therefore, the importance of various factors underlying effective collaborative learning should be determined. In the current study, five different life sciences undergraduate courses with successful collaborative-learning results were selected. This study focuses on factors that increased the effectiveness of collaboration in these courses, according to the students. Nine focus group interviews were conducted and analyzed. Results show that factors evoking effective collaboration were student autonomy and self-regulatory behavior, combined with a challenging, open, and complex group task that required the students to create something new and original. The design factors of these courses fostered a sense of responsibility and of shared ownership of both the collaborative process and the end product of the group assignment. In addition, students reported the absence of any free riders in these group assignments. Interestingly, it was observed that students seemed to value their sense of achievement, their learning processes, and the products they were working on more than their grades. It is concluded that collaborative learning in higher education should be designed using challenging and relevant tasks that build shared ownership with students.

INTRODUCTION

Students may learn a lot from working in groups, but the learning potential of collaboration is underused in practice ( Johnson et al ., 2007 ), particularly in science education ( Nokes-Malach and Richey, 2015 ). Collaborative, cooperative, and team-based learning are usually considered to represent the same concept, although they are sometimes defined differently ( Kirschner, 2001 ); we consider these concepts comparable and use the term “collaboration” throughout the paper. In collaborative learning, students participate in small-group activities in which they share their knowledge and expertise. In these student-driven activities, the teacher usually acts as a facilitator ( Kirschner, 2001 ).

Several decades of empirical research have demonstrated the positive relationship between collaborative learning and student achievement, effort, persistence, and motivation (for reviews, see Slavin, 1990 ; Webb and Palinscar, 1996 ; Barron, 2000 ; Johnson et al ., 2007 ). Collaborative learning potentially promotes deep learning, in which students engage in high-quality social interaction, such as discussing contradictory information ( Visschers-Pleijers et al ., 2006 ). In science education, a deep-learning approach is crucial for understanding concepts and complex processes ( Van Boxtel, 2000 ). Understanding of these concepts involves a process of conceptual change, a process particularly activated in collaborative learning, whereby students interact by explaining to and questioning one another critically ( Van Boxtel et al ., 2000 ; Linton et al ., 2014 ). In previous papers, we have explored and emphasized the relevance of collaborative learning in undergraduate biology courses ( Wiegant et al ., 2012 , 2014 ). By comparing university student achievement in a biology course in individual and group settings, Linton et al . (2014) found that students in group settings achieved significantly better with respect to conceptual understanding in comparison with students in courses with an individual setting. Besides these cognitive benefits, collaborative learning provides social skills needed for future professional work in the field of science.

Just forming groups, however, does not automatically result in better learning and motivation ( Salomon and Globerson, 1989 ; Gillies, 2004 ; Khosa and Volet, 2013 ). In their study of university students’ preferences for collaborative learning, Raidal and Volet (2009) found an overwhelming preference for individual forms of learning. Students are hesitant about group work because of the occurrence of “free riders,” logistical issues, or interpersonal conflicts ( Livingstone and Lynch, 2000 ; Aggarwal and O’Brien, 2008 ; Pauli et al ., 2008 ; Shimazou and Aldrich, 2010 ; Hall and Buzwell, 2012 ). As a result, students might opt for a strategic approach by dividing the work and merely using a stapler to “integrate” their work into a group paper. Johnson and Johnson (1999) refer to groups showing this kind of superficial behavior as “pseudo learning groups.” In turn, the resulting lack of synthesis can be disappointing for teachers. Dividing work also implies that students lose the potential learning effect of collaborating, since the extent to which students benefit from working with other students depends on the quality of their interactions ( Van Boxtel et al ., 2000 ; King, 2002 ; Palinscar and Herrenkohl, 2002 ; Volet et al ., 2009 ). Insight into factors that facilitate collaborative learning is critical for understanding how collaboration can be used effectively in higher education. Therefore, in the present study, we explore factors that optimize the quality of collaboration, using examples of effective group work in five different life sciences courses.

POTENTIAL FACTORS ENHANCING THE EFFECTIVENESS OF COLLABORATIVE LEARNING

Social interaction is crucial for effective collaboration ( Volet et al ., 2009 ). Learning outcomes of collaborative-learning groups have been found to depend on the quality of student discussions, including argumentation ( Teasley, 1995 ; Chinn et al ., 2000 ), explaining ideas to one another ( Veenman et al ., 2005 ), and incorporating and building on one another’s ideas ( Barron, 2003 ). These interactions with peers are assumed to promote students’ cognitive restructuring ( Webb, 2009 ). Explaining things to one another and discussing subject matter may lead to deeper understanding, to recognition of misconceptions, and to the strengthening of connections between new information and previously learned information ( Wittrock, 1990 ). The question of how to organize collaboration in a way that promotes these kinds of interactions is paramount.

Decades of research on group work have resulted in the identification of various factors that potentially enhance the effectiveness of collaboration. These factors can be differentiated as primary factors (design characteristics) and secondary or mediating factors (group-process characteristics). Regarding primary factors, groups need to be small (three to five students) to obtain meaningful interaction ( Lou et al ., 2001 ; Johnson et al ., 2007 ). With respect to group composition, mixed-ability groups have been found to increase performance for students of lower ability, but this composition does not necessarily benefit high-ability students ( Webb et al ., 2002 ). Equal participation, however, has been shown to be more important for students’ achievement than group composition, because students are more likely to use one another’s knowledge and skills fully when all students participate to the same extent ( Woolley et al ., 2015 ). Heterogeneity, with respect to diversity of perspectives and styles, has been found to increase learning, particularly in groups working on tasks that require creativity ( Kozhevnikov et al ., 2014 ). The nature of the task has been shown to be an important factor as well. Open and ill-structured tasks promote higher-level interaction and improve reasoning and applicative and evaluative thinking to a greater extent than closed tasks ( Gillies, 2014 ). In addition, complex tasks provoke deeper-level interactions than simple tasks ( Hertz-Lazarowitz, 1989 ).

Concerning secondary or intermediate factors affecting group work, positive interdependence theory is one of the best-founded theories explaining the quality of interaction in collaborative learning ( Slavin, 1990 ; Johnson and Johnson, 1999 , 2009 ; Gully et al ., 2002 ). According to this theory, collaboration is enhanced when positive interdependence exists among group members. This is achieved when students perceive the contribution of each individual to be essential for the group to succeed in completing the assigned activity ( Johnson and Johnson, 2009 ). Positive interdependence results in both individual accountability and promotive interaction. Individual accountability is defined as having feelings of responsibility for completing one’s own work and for facilitating the work of other group members. A sense of mutual accountability is necessary to avoid free riding ( Johnson and Johnson, 2009 ), which occurs when one or more group members are perceived by other members as failing to contribute their fair share to the group effort ( Aggarwal and O’Brien, 2008 ). Promotive interaction has been described as students encouraging and facilitating one another’s efforts to accomplish group goals, both with respect to group dynamics and the subject matter ( Johnson and Johnson, 2009 ).

Methods of inducing positive interdependence interaction are either reward or task based ( Johnson et al ., 2007 ). Reward-based interdependence structures the reward in such a way that students’ individual grades depend on the achievement of the whole team. According to Slavin (1991 , 1995 ), collaborative learning is rarely successful without group rewards. In higher education, however, findings on the effects of reward-based interdependence are inconclusive. The main concern is that rewards stimulate extrinsic motivation and may be detrimental to intrinsic motivation ( Parkinson and St. George, 2003 ). Intrinsically motivated students put effort into a task because they are interested in the task itself, while extrinsically motivated students are interested in the reward or grade ( Deci and Ryan, 2000 ). Strong incentives, such as grades, could steer student motivation toward the reward and subsequently reduce the task to being a means to an end. Serrano and Pons (2007) , however, found that using rewards (individual grades) created high positive interdependence in group work at a university level. They concluded that the reward structure did direct students’ motivation toward final grades, while the task still aroused the interest of the students. In contrast, Sears and Pai (2012) found that rewards were not crucial factors affecting group behavior. Their study showed that groups continued to work even after the reward was removed, whereas the efforts of students working individually decreased after the reward was removed.

In structured task-based interdependence, students are forced to exchange information; this can be achieved by assigning group members different roles, resources, or tasks (the “jigsaw” method) or by “scripting” the process, which involves giving students a set of instructions on how they should interact and collaborate ( Kagan, 1994 ; Dillenbourg, 2002 ). The effects of task structuring on collaborative learning are, however, not clear ( Fink, 2004 ; Hänze and Berger, 2007 ; Serrano and Pons, 2007 ). Hänze and Berger (2007) observed no differences in achievement between students who worked in jigsaw-structured groups and students who worked individually. In contrast, the observations of Brewer and Klein (2006) indicated that students in groups with given roles plus rewards interacted significantly more frequently than students in groups with given rewards only or in groups without structured interdependence factors. (Over)structuring interaction processes, on the other hand, could threaten intrinsic motivation and disturb natural interaction processes ( Dillenbourg, 2002 ). Although it is widely accepted that positive interdependence has been shown to be crucial in evoking social interaction, in practice, university students often tend to merely go through the motions and choose the solution requiring the least effort, which explains why positive interdependence often does not emerge ( Salomon and Globerson, 1989 ). Additional methods are necessary to encourage quality interactions that enhance learning. Moreover, the mixed results of university education studies concerning structuring interdependence—using either rewards or task structuring—do not solve the challenge of how to create interdependence without disturbing the intrinsic motivation of students. Forcing students to interact could endanger student autonomy and motivation, while merely putting students together has been shown to be ineffective.

THE CURRENT STUDY

Despite the considerable amount of research on collaborative learning, less is known about how to structure university-level group work in order to capitalize on the benefits of collaborative learning. The studies discussed earlier focused on primary and secondary education and are not fully applicable to higher education, because students in undergraduate classes may have different schedules and often have not met before. Moreover, group work of university students is mostly organized outside class hours in the absence of teachers. Furthermore, literature in this area may be limited in applicability, as many studies of factors affecting collaboration have used (quasi)experimental designs, in which outcomes of two or three designs were compared ( Johnson and Johnson, 2009 ). A restriction of this method is that only the hypothesized independent variables are studied, while other important factors contributing to effectiveness might be overlooked. In our study, we approached the theme retrospectively, investigating the learning of student groups known to have collaborated and achieved highly, according to their teachers. Rather than focusing on learning outcomes, we explored how group work in these courses was structured. Understanding the factors that facilitate students’ collaboration is critical to understanding how this approach to learning can be used more effectively in higher education. We explicitly focused on positive examples of effective collaborative learning, as best practices should be communicated to others ( Dewey, 1929 , p.11).

In the current study, we selected five different life sciences undergraduate courses that comprised successful group-work assignments. The specific question this study aimed to address was, according to the students, what factors increased collaboration in these courses? By uncovering the factors that make collaborative learning fruitful, we aim to provide useful guidelines for instructors implementing collaborative learning.

Participants

The present study involved focus group interviews with nine groups of second- and third-year students of five different undergraduate life sciences courses. We depended heavily on these focus group interviews to develop our understandings. They allowed us to gain insight into students’ perspectives, which is important because, to a large degree, students’ perspectives of instruction affect what they do and learn ( Shuell, 1996 ). Furthermore, the group exchanges of experiences and perspectives promoted breadth, as well as depth, in our understandings of the cognitive, behavioral, and situational factors contributing to the effectiveness of the collaboration. The particular courses were selected because they all implemented group work that, according to teacher assessments and student evaluations, was very effective. We approached the instructors of these courses with the request to ask their students to volunteer in focus group discussions. Students were willing to participate in these focus group discussions, although not all students were able to meet at the scheduled times. No specific reward was promised for participating in focus group discussions.

Between two and 10 students participated in each of the nine focus group interviews (see Table 1 ).

Course, number of focus group interviews, and students per interview

Course Descriptions

We focused on five courses that were all small-enrollment, upper-division courses in which 15–35 students participated per course. In all courses, collaborative activities occurred during class hours but also outside of class. In some courses, the out-of-class cooperative activities even exceeded the in-class activities.

Course A: The first course was part of a biology honors program. In this part of the program, groups of second-year bachelor’s students (12–19 students) were assigned the group task of writing a popular science book about a biology topic of their choice. Students had to perform all the activities necessary to produce the book. The project was strongly student-led, and students assigned themselves tasks necessary for finishing the project. The assignment comprised an entire academic year, starting in September and finishing in May/June as an extracurricular activity. More details of this course are described elsewhere ( Wiegant et al ., 2012 ).

Course B: Students in the immunology course, mostly third-year students, were assigned the task of writing, in groups of four, a short research project on an immunological topic. The assignment was structured in three parts: in part 1, groups designed a draft of their proposal; in part 2, the groups peer reviewed the draft of another group; and in part 3, the groups received the draft and comments of yet another group, which they had to finish and present. The assignment comprised approximately half of the course.

Course C: In the advanced cell biology course, three small teams of four or five students collaborated intensively during a semester of 15 weeks to formulate three PhD proposals within an overarching theme. Because the course was student-led, the teachers refrained from guiding the students in their decisions, instead taking a facilitating role by asking critical questions and providing feedback. As a result of the project, the teams presented and defended their research program and the three research proposals before a jury of experts. More details of this course are given elsewhere ( Wiegant et al ., 2011 , 2014 ; Scager et al ., 2014 ).

Course D: The objective of the molecular cell biology course was to learn to design a research project in groups of four. In this course, students were required to complete multiple assignments, such as reviewing a paper, developing a research proposal, designing experiments, and writing and defending their proposals. Groups met with their supervisor once a week and were supposed to keep the course coordinator informed on their progress. Final grades were based on individual (40%) and group (60%) components.

Course E: As a part of the pharmacy course, third-year students, in groups of four to six participants, were required to analyze the quality of a specific pharmacotherapy. The assignments were authentic and were provided by external commissioning companies. The group assignment counted for 70% of the final grade (50% group report and presentation; 20% individual reflection).

The interviews were semistructured and included two basic questions: 1) “What factors made group work effective in this course (as opposed to other experiences you have had)?” and 2) “What was the added value in this course of working in a group (as opposed to working individually)?” The addition of “as opposed to …” was aimed to encourage students’ thinking process; we did not ask students to elaborate on these opposing experiences. Interviewers stimulated and moderated discussions, ensuring depth as well as diversity. To focus and structure the interviews and to stimulate the sharing of discussion outcomes, we listed the answers to the two questions on a flip chart.

First, the intentions of the interview were clarified, followed by an explanation of the confidential nature of the interview. All students agreed and gave permission for the interviews to be audiotaped. All of the authors conducted one or more interviews, with the first author (K.S.) moderating them. The focus group interviews were held in or near the classroom associated with each of the specific courses. The interviews were ∼60 minutes each and were transcribed verbatim.

Detecting Factors That Facilitated Group Work.

Data were analyzed by the first and fourth authors (K.S. and J.V.) in three partially overlapping stages. Stage 1 comprised reading and rereading the transcripts to identify text units relevant to the subject of challenge. Given the aim of the focus group interviews, this meant ignoring small talk and sorting discussion units related to the two interview questions into focal issues. Stage 2 comprised identifying and coding themes related to the two main interview questions regarding 1) factors and 2) added value, using NVivo version 10 (a qualitative data-analysis computer software package). First, open coding was applied. The answers to both questions, however, evoked answers that pointed to intermediary variables affecting the outcomes of collaboration. For example, the question regarding factors brought forward the importance of the assignment being complex enough to make students feel mutually interdependent, while for the question regarding added value, students referred back to how the complexity of the assignment stimulated them to discuss, build on, and learn from one another’s ideas. The interactions provoked by the complexity of the task seemed to connect complexity with learning outcomes. Therefore, when axial coding was applied, we decided to develop three clusters of codes focused on the factors of effective collaboration, the mediating variables, and the added value of collaboration. Subsequently, selective coding was applied, wherein codes were clustered into larger sets informed by theory ( Braun and Clarke, 2006 ). Only factors that were mentioned in more than half of the focus groups were kept. This resulted in two sets of factors. The first set of factors related to the design of the group assignment (autonomy, group size, task design, and teacher expectations). The second set consisted of mediating variables related to the working processes of the groups (team and task regulation, promotive interaction, interdependence, responsibility, and mutual support and motivation).

Reliability and Validity.

Reliability is considered in terms of equivalence and internal consistency ( Sim and Wright, 2000 ). Reliability was ensured by intercoder consistency ( Burla et al ., 2008 ). Given the complexity and inhomogeneity of group discourse, agreement testing was constrained to core concepts or themes of substantive importance ( Kidd and Parshall, 2000 ). The equivalence of coding was addressed by selecting 20% of the data and comparing the coding of two secondary raters (10% each) for consistency, which yielded a kappa coefficient of 0.85. This strength of agreement is considered to be “nearly perfect” ( Everitt, 1996 ). Internal consistency was acquired by having one team member moderating all (but one) of the interviews ( Kidd and Parshall, 2000 ). The emergence of substantively similar viewpoints of the focus groups on the core issues across the five different courses supported content validity ( Kidd and Parshall, 2000 ). Furthermore, we assessed content validity by independent coding and by comparing this with theory in extant literature ( Morgan and Spanish, 1985 ; Torn and McNichol, 1998 ).

Factors That Contributed to the Effectiveness of the Collaboration

Eight factors were found to have a positive effect on the effectiveness of the collaboration. These factors are presented in Table 2 : 1) design factors: the design of the course and/or the assignment (the autonomy of the students, task characteristics, teacher expectations, and group size); and 2) process factors: the way students interacted and organized their work (team and task regulation, interdependence, promotive interaction, and mutual support and motivation).

Factors that contributed to the effectiveness of the collaboration

a “Source” refers to how many of the nine interviews the topic was discussed in; “reference” refers to the total number of times the topic was discussed.

Table 2 shows that autonomy and the density and complexity of the task were the factors most frequently mentioned by the students as contributing to the effectiveness of the collaboration. Team and task regulation, positive interdependence, and promotive interaction were perceived by students as the most important factors with respect to the way they processed the assignments. In the next section, we describe the results more elaborately, starting with the design features of these courses that are considered to enhance collaboration processes.

Design Factors

The autonomy the groups experienced was mentioned in all focus groups, indicating the importance of this factor to the effectiveness of collaboration. Autonomy was manifested in allowing student groups to choose their own topics (e.g., for their research plans) and giving them independence in organizing their processes. Statements such as “It was our own thing” occurred frequently in all nine focus group discussions. The references to “our thing” indicate that the students made choices as a group, which could have restricted individual feelings of autonomy. The students, however, did not seem to have experienced clear boundaries between individual and group autonomy. Even though their personal ideas may have been overruled by the team, they still felt autonomous, because they made decisions democratically. As one of the students said, “When you participate in the decision process it is easier to accept than when the decision is made by the teacher.”

Two features of the task were perceived as important contributors to the effectiveness of the group work. First, the density and complexity of the task was crucial. The group task needed to be extensive enough for the group members to really need one another’s contributions to finish in time and complex enough to require them to discuss their work and provide one another with feedback. Second, students perceived the relevance of the task at hand to be an important feature. The task relevance was found in different aspects, depending on the assignment. For the biology honors groups, for example, the process of writing a popular science book and getting it published increased their feelings of doing something significant. The cell biology and immunology groups emphasized the relevance of doing research, in terms of formulating a relevant proposal in the same way as it is done “in the real world.”

In terms of rewards , students emphasized that the inherent value of the end product, such as an article, a research proposal, or a book, stimulated them to achieve, which relates back to the perceived task relevance. As a student of the biology honors course said, “We have also had other group projects …, but that was taken less seriously, because you, well it was nice, but well, the result wouldn’t reach beyond the classroom, while in this project it will.” There were no grades involved in this particular course, which students appreciated, because they believed the end product to be more important than a grade. Also, in other groups, discussions about assessment were learning and/or reward oriented rather than grade oriented; for example, in one of the pharmacy groups it was said: “You are in a learning process, and I think sometimes that it is a shame that it should end in a grade—that creates a tension. And if things go wrong, that could be very beneficial for your learning, but it can also happen that you do not receive a high grade for it.”

In all of the interviews, students mentioned that it was crucial that the task was the core project in the course at that time, as students of the immunology course stated: “I think also because this is not something you do on the side, but this is the only thing we do at the moment, it is the main activity.” The fact that students’ final grades depended primarily on the group assignment was mentioned in some groups. Students emphasized that in previous experiences with group assignments they had not collaborated as intensively because their final grade did not depend largely on the team assignment.

Finally, group size was considered a factor stimulating collaboration in seven of the groups, specifically related to the level of responsibility students felt. Groups of three or four were believed to be optimal: “Otherwise, you get a sort of diffuse responsibility …, and with four you are clearly responsible for an important part of the process.”

Process Factors

The need for team and task regulation was mentioned most frequently in the focus group discussions as an important factor increasing the effectiveness of collaboration. Students divided tasks, appointed team leaders, and set their own deadlines. Organizing frequent face-to-face meetings was very helpful, according to students: “That we met each other physically, instead of doing everything by mail or chat, like in other projects. This works much better, if you can look each other in the eyes it is way faster and more efficient to manage and decide things …. It also increases the pressure, everybody prepares for a meeting.” The quote in Table 2 indicates the direct relation between the autonomy of the groups and their dedication to following their self-made group regulations.

As shown in Table 2 , students in all nine focus groups experienced a sense of positive interdependence in terms of needing one another in order to succeed and achieve their goal. The feeling of responsibility was discussed in six groups. The related issue of “uneven contribution” was discussed in all nine of the focus groups: students did experience differences in power and effort between team members. Interestingly, students did not perceive this as free riding. According to the students, some degree of uneven contribution is only natural; the students all did their best, but as the students said, “There weren’t students who contributed less; there were only students who contributed more.” According to the students, this uneven contribution was due to power differences, not to disinterest or laziness. Students showed empathy for their peers who contributed less: “The strong people might go too hard for the other people to be able to catch up.” This may have caused frustration in students who felt they were lagging behind, as one of them revealed: “You have that responsibility that drives you and then you feel the need to do more, but perhaps that is beyond your capabilities at that point.” Some of the groups discussed the issue of uneven contribution while working on their projects, but always, they stated, in an “understanding and respectful way.” Furthermore, students in all nine interviews mentioned the fact that the variety among students was useful and enhanced the discussions: “working in a group consisting of clones of yourself” would not be as interesting, one of the pharmacy groups stated.

All nine groups mentioned the need for promotive interaction several times, drawing attention to the need to discuss content to accomplish team goals. They mentioned several indicators of promotive interaction: discussions, exchange of information, and arguments, building on one another’s ideas, explaining to one another, providing and processing peer feedback, and asking one another critical questions. According to the students, these discussions enhanced their understanding, and they also learned how to discuss, voice their opinion, explain, listen to others, accept feedback, and reflect on their own work.

Last, but not least, students talked enthusiastically about the way they supported and motivated one another. There was explicit help and pep talks, and, perhaps even more importantly, implicit mutual inspiration effected by them perceiving the motivation of their peers.

Finally, we found one contextual factor (not included in Table 2 ) contributing to collaboration: the shared motivation of students to get the best out of the group assignment. Students mostly linked their having similar motivations to the fact that they were in their second or third year (four of the five courses were third-year courses). First, the students already knew one another: “When you are in your first year, you do not know each other, and some people are a bit insecure, so to say. But now we know each other, so we may scold each other all we can.” Furthermore, students suggested being equally motivated, because the unmotivated students had already left in previous years.

CONCLUSIONS AND DISCUSSION

The purpose of the current study was to find factors that enhance student collaboration. The collaboration processes (task and team regulation, mutual support and motivation, positive interaction) used by these students were distinctly effective. During these processes, positive interdependence was clearly present, supporting the notion that positive interdependence is a crucial factor affecting the effectiveness of collaboration ( Johnson and Johnson, 2009 ). Although the interview data do not allow causal relations between design factors and collaboration processes to be inferred, it seems reasonable to assume that positive interdependence was evoked by a combination of the nature of the task (autonomous, relevant, dense and complex, group rewards), the prominent placement of the group assignment within the course, and the group size.

The results indicate that positive interdependence was an important factor contributing to the effectiveness of collaboration. The positive effect of interdependence on student achievement has already been well documented (for reviews, see Slavin, 1990 ; Webb and Palinscar, 1996 ; Johnson et al ., 2007 ). Although we disassembled the factors contributing to collaboration in the analysis , we assume interdependence does not consist of a single factor but rather is constructed through the interaction between motivated students and design factors (the nature of the task and student autonomy). Furthermore, the fact that the final grade depended primarily on the group assignment can be expected to have contributed to students’ interdependence, which would concur with the findings of Slavin (1991) . Interestingly, however, these students seemed to value the learning process and the products they were working on more than their grades. Our finding, that a sense of achievement rather than a grade was of greater importance in motivating interdependence, contradicts findings of Slavin (1991) and Tsay and Brady (2010) . Tsay and Brady (2010) found that the degree of active participation of university students in collaborative groups was affected by the importance they attached to grades: students who perceived grades as highly important were more active collaborators.

The enthusiasm of the students when speaking of the way they supported and motivated one another and regulated the team and task processes indeed indicates the occurrence of strong self-regulatory processes. Although some structure was provided beforehand in all five courses (e.g., final deadlines), students were perceived to be autonomous in the planning and regulation of their work, which they said added to their motivation to follow their own rules and planning. This direct relationship between perceived autonomy and self-regulatory behavior is aligned with self-determination theory ( Deci and Ryan, 2000 ). According to Deci and Ryan (2000) , when teachers are supportive of student autonomy, students are motivated to internalize the regulation of their learning activities, whereas when teachers are controlling, self-regulated motivation is undermined. The self-regulatory social processes of these students, encouraged by the autonomy they were provided, were the most important factors increasing the effectiveness of their collaboration in these five cases.

Individual accountability is an important aspect within the theory of positive interdependence. Interestingly, instead of accountability, students used the word “responsibility.” The difference between responsibility and accountability is meaningful, because accountability is focused on the end result, or being answerable for your actions to relevant others, while responsibility is related to the task. Responsibility is viewed as having a higher level of autonomy and involves the ability to self-regulate actions free of external motivational pressure. In contrast, the accountable actor is subject to external oversight, regulation, and mechanisms of punishment ( Bivins, 2006 ). The term “responsibility” more appropriately fits the collaboration in these cases, as one of our participants illustrates: “You feel the responsibility to other people in your group, because as soon as soon as you drop the ball, the rest have to work harder.” This student does not refer to consequences externally imposed on him, but he feels responsibility toward others. The effect this has may be the same as when students are forced to be accountable because of reward- or task-based structures, as suggested by Johnson and Johnson (2009) ; however, the nature of the motivation is more intrinsically than extrinsically induced.

Related to the issue of accountability or responsibility is the problem of free riding, which is one of the main problems of group work in higher education ( Livingstone and Lynch, 2000 ; Aggarwal and O’Brien, 2008 ; Pauli et al ., 2008 ; Shimazou and Aldrich, 2010 ). In the interviews in which the issue of free riding came up, however, groups did not seem to have experienced the phenomenon. A putative explanation for the lack of free-riding behavior is the incidence of accountability ( Slavin, 1991 ; Johnson and Johnson, 2009 ; Onwegbuezie et al ., 2009 ), as students definitely felt responsible for the end result. The way students spoke about their group members, however, was in terms of mutual trust rather than accountability. Students recognized differences in contribution but did not perceive this as problematic. They were empathic toward differences between students. If there were negative feelings at all, the low contributors were more apt to feel frustrated, indicating that the differences in contribution were, as Hall and Buzwell (2012) have suggested, involuntary and due to inadequacy rather than apathy or laziness.

In the five courses of this study, the combination of design factors seems to have prevented free riding. Although the causal nature of the relationship between design features of the group work and effective group processing cannot be claimed in the current study, the results indicate that, in particular, perceived autonomy and the challenging nature of the task evoked students’ motivation to make an effort. The relevance of the tasks, which required students to produce something new (to them) and something original and tangible, motivated students. The tasks were also open and complex, which are features that have already been found to promote deeper-level interactions than simple tasks ( Hertz-Lazarowitz, 1989 ; Cohen, 1994 ). Autonomy was a factor frequently mentioned as contributing to the effectiveness of the group work. Contradictory to Johnson and Johnson’s (2009) recommendation for teachers to structure processes, students of these courses designated the autonomy they had in choosing their topic and in organizing the process, as one of the factors increasing their motivation. Results from organizational research show that autonomy can, in fact, increase teamwork achievement, but only when positive interdependence is high ( Langfred, 2000 ). Autonomy combined with low interdependence decreases achievement, indicating that autonomy should be combined with challenging tasks. Although autonomy and level of challenge in a group assignment appears to be vital, instructors in different settings may need to use greater scaffolding.

Future Research and Concluding Remarks

It is important to keep in mind the small sample and restricted context when interpreting these findings. Although the results have been obtained in small-enrollment, upper-division courses, we think that our findings might also be transferable to large-enrollment courses, provided students will be working in self-directed small groups on substantial and relevant projects. As generalizability requires data on large populations, the findings of our five cases within a restricted context are not necessarily representative of the larger population. We believe, however, that there are strong reasons for our findings to be deemed “transferable” ( Lincoln and Guba, 1985 ) to comparable situations. While generalization is applied by researchers, transferability is a process performed by the readers of research ( Metcalfe, 2005 ). Unlike generalizability, transferability does not involve broad claims but invites readers of research to make connections between elements of a study and their own experiences ( Barnes et al ., 2012 ). According to Berliner (2002 , p. 19), implementing scientific findings is always difficult in education, “because humans in schools are embedded in complex and changing networks of social interaction.” Therefore, we do not claim to have produced broadly generalizable findings but instead invite the reader to identify how the findings can be transferred to his or her situation. Similar studies with data from other university contexts, such as other countries or other class settings, would help in understanding how the conditions that facilitate collaborative learning relate to different settings.

We assume, however, that the concept of evoking, rather than enforcing, positive interdependence by increasing autonomy and the challenge level of the task provides relevant insights for discourse on effective design of group work within life sciences education. Students in life sciences education, in general, are quite experienced in working in groups and in regulating their own work. Autonomy, combined with a challenging task, evoked interdependence and generated interaction as well as student motivation in these five cases. Structuring the process, for example by scripting, seems unnecessary for promoting student interaction. It was, in Dillenbourg’s (2002) words, not necessary to “didactisise” collaborative interactions or to disturb the autonomy and natural interactions of students. Moreover, structuring the process could have impeded the feeling of autonomy, which is crucial for student motivation (Deci and Ryan, 2000). Brewer and Klein (2006) came to a similar conclusion in their investigation of the influence of types of interdependence (roles, rewards, roles plus rewards, no structure) on student interaction. The groups with no structured interdependence had significantly more cognitive interactions involving content discussion than the other groups, indicating that structuring interdependence is not always necessary with university students. We suggest that collaborative learning with university students should be designed using challenging and relevant tasks that build shared ownership with students.

Acknowledgments

Drs. Kristin Denzer, Mario Stassen, and Fons Cremers are gratefully acknowledged for encouraging their students to participate in the interviews.

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Influence of e-learning on the students’ of higher education in the digital era: A systematic literature review

• Published: 16 April 2024

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• Rashmi Singh   ORCID: orcid.org/0000-0001-9195-5301 1 ,
• Shailendra Kumar Singh 1 &
• Niraj Mishra 1  

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The integration of digital technologies into educational practices has reshaped traditional learning models, creating a dynamic and accessible global landscape for higher education. This paradigm shift transcends geographical boundaries, fostering a more interconnected and inclusive educational environment. This comprehensive literature analysis explores the impact of e-learning on higher education students in the digital era. A meticulous review of 53 studies, sourced from reputable databases including Web of Science, Taylor & Francis, Springer Link, ProQuest, Elsevier, and Scopus, was conducted. Employing the content analysis method, the selected studies spanning from November 2012 to April 2023 were systematically examined. Predominantly utilizing quantitative methods, the studies, largely originating from the United States, China, Malaysia, and India, focused on university students. Key variables such as student engagement, perception, and academic performance were consistently employed across diverse educational settings. The synthesis of findings revealed that e-learning technologies positively impacted academic achievement, student satisfaction, and collaborative efforts. Moreover, challenges associated with technology usage and internet access were identified, which impact e-learning implementation. The study proposes further investigation through a mixed-methods approach to explore students’ interactions with the educational environment while utilizing e-learning technology in institutions of higher education.

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Singh, R., Singh, S.K. & Mishra, N. Influence of e-learning on the students’ of higher education in the digital era: A systematic literature review. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12604-3

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