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In This Article Expand or collapse the "in this article" section Case Study in Education Research

Introduction, general overview and foundational texts of the late 20th century.

• Conceptualisations and Definitions of Case Study
• Case Study and Theoretical Grounding
• Choosing Cases
• Methodology, Method, Genre, or Approach
• Case Study: Quality and Generalizability
• Multiple Case Studies
• Exemplary Case Studies and Example Case Studies
• Criticism, Defense, and Debate around Case Study

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Case Study in Education Research by Lorna Hamilton LAST REVIEWED: 21 April 2021 LAST MODIFIED: 27 June 2018 DOI: 10.1093/obo/9780199756810-0201

It is important to distinguish between case study as a teaching methodology and case study as an approach, genre, or method in educational research. The use of case study as teaching method highlights the ways in which the essential qualities of the case—richness of real-world data and lived experiences—can help learners gain insights into a different world and can bring learning to life. The use of case study in this way has been around for about a hundred years or more. Case study use in educational research, meanwhile, emerged particularly strongly in the 1970s and 1980s in the United Kingdom and the United States as a means of harnessing the richness and depth of understanding of individuals, groups, and institutions; their beliefs and perceptions; their interactions; and their challenges and issues. Writers, such as Lawrence Stenhouse, advocated the use of case study as a form that teacher-researchers could use as they focused on the richness and intensity of their own practices. In addition, academic writers and postgraduate students embraced case study as a means of providing structure and depth to educational projects. However, as educational research has developed, so has debate on the quality and usefulness of case study as well as the problems surrounding the lack of generalizability when dealing with single or even multiple cases. The question of how to define and support case study work has formed the basis for innumerable books and discursive articles, starting with Robert Yin’s original book on case study ( Yin 1984 , cited under General Overview and Foundational Texts of the Late 20th Century ) to the myriad authors who attempt to bring something new to the realm of case study in educational research in the 21st century.

This section briefly considers the ways in which case study research has developed over the last forty to fifty years in educational research usage and reflects on whether the field has finally come of age, respected by creators and consumers of research. Case study has its roots in anthropological studies in which a strong ethnographic approach to the study of peoples and culture encouraged researchers to identify and investigate key individuals and groups by trying to understand the lived world of such people from their points of view. Although ethnography has emphasized the role of researcher as immersive and engaged with the lived world of participants via participant observation, evolving approaches to case study in education has been about the richness and depth of understanding that can be gained through involvement in the case by drawing on diverse perspectives and diverse forms of data collection. Embracing case study as a means of entering these lived worlds in educational research projects, was encouraged in the 1970s and 1980s by researchers, such as Lawrence Stenhouse, who provided a helpful impetus for case study work in education ( Stenhouse 1980 ). Stenhouse wrestled with the use of case study as ethnography because ethnographers traditionally had been unfamiliar with the peoples they were investigating, whereas educational researchers often worked in situations that were inherently familiar. Stenhouse also emphasized the need for evidence of rigorous processes and decisions in order to encourage robust practice and accountability to the wider field by allowing others to judge the quality of work through transparency of processes. Yin 1984 , the first book focused wholly on case study in research, gave a brief and basic outline of case study and associated practices. Various authors followed this approach, striving to engage more deeply in the significance of case study in the social sciences. Key among these are Merriam 1988 and Stake 1995 , along with Yin 1984 , who established powerful groundings for case study work. Additionally, evidence of the increasing popularity of case study can be found in a broad range of generic research methods texts, but these often do not have much scope for the extensive discussion of case study found in case study–specific books. Yin’s books and numerous editions provide a developing or evolving notion of case study with more detailed accounts of the possible purposes of case study, followed by Merriam 1988 and Stake 1995 who wrestled with alternative ways of looking at purposes and the positioning of case study within potential disciplinary modes. The authors referenced in this section are often characterized as the foundational authors on this subject and may have published various editions of their work, cited elsewhere in this article, based on their shifting ideas or emphases.

Merriam, S. B. 1988. Case study research in education: A qualitative approach . San Francisco: Jossey-Bass.

This is Merriam’s initial text on case study and is eminently accessible. The author establishes and reinforces various key features of case study; demonstrates support for positioning the case within a subject domain, e.g., psychology, sociology, etc.; and further shapes the case according to its purpose or intent.

Stake, R. E. 1995. The art of case study research . Thousand Oaks, CA: SAGE.

Stake is a very readable author, accessible and yet engaging with complex topics. The author establishes his key forms of case study: intrinsic, instrumental, and collective. Stake brings the reader through the process of conceptualizing the case, carrying it out, and analyzing the data. The author uses authentic examples to help readers understand and appreciate the nuances of an interpretive approach to case study.

Stenhouse, L. 1980. The study of samples and the study of cases. British Educational Research Journal 6:1–6.

DOI: 10.1080/0141192800060101

A key article in which Stenhouse sets out his stand on case study work. Those interested in the evolution of case study use in educational research should consider this article and the insights given.

Yin, R. K. 1984. Case Study Research: Design and Methods . Beverley Hills, CA: SAGE.

This preliminary text from Yin was very basic. However, it may be of interest in comparison with later books because Yin shows the ways in which case study as an approach or method in research has evolved in relation to detailed discussions of purpose, as well as the practicalities of working through the research process.

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The use of data science for education: The case of social-emotional learning

• Ming-Chi Liu 1 &
• Yueh-Min Huang 1  

Smart Learning Environments volume  4 , Article number:  1 ( 2017 ) Cite this article

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The broad availability of educational data has led to an interest in analyzing useful knowledge to inform policy and practice with regard to education. A data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions. Consequently, the purpose of this paper is first to incorporate an appropriate data-analytic thinking framework for pursuing such goals. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.

Introduction

Recently, AlphaGo, an artificially intelligent (AI) computer system built by Google, was able to beat world champion Lee Sedol at a complex strategy game called Go. AlphaGo’s victory shocked not only artificial intelligence experts, who thought such an event was 10 to 15 years away, but also educators, who worried that today’s high-value human skills will rapidly be sidelined by advancing technology, possibly even by 2020 (World Economic Forum 2016 ). Such potential technologies also catch some reflections of the relevance of certain educational practices in the future.

At the same time, emerging AI technologies not only pose threats but also create opportunities of producing a wide variety of data types from human interactions with these platforms. The broad availability of data has led to increasing interest in methods for exploring useful knowledge relevant to education—the realm of data science (Heckman and Kautz 2013 ; Levin 2013 ; Moore et al. 2015 ). In other words, data-driven decision-making through the collection and analysis of educational data is increasingly used to inform policy and practice, and this trend is only likely to grow in the future (Ghazarian and Kwon 2015 ).

The literature on education data analytics has many materials on the assessment and prediction of students’ academic performance, as measured by standardized tests (Fernández et al. 2014 ; Linan and Perez 2015 ; Papamitsiou and Economides 2014 ; Romero and Ventura 2010 ). However, research on education data analytics should go beyond explaining student success with the typical three Rs (reading, writing and arithmetic) of literacy in the current economy (Lipnevich and Roberts 2012 ). Furthermore, the availability of data alone does not ensure successful data-driven decision-making (Provost and Fawcett 2013 ). Consequently, there is an urgent need for further research on the use of an appropriate data-analytic thinking framework for education. The purpose of this paper is first to identify research goals to incorporate an appropriate data-analytic thinking framework for pursuing such goals, and second to present a case study of social-emotional learning in which we used the data science research methodology.

Defining data science

Dhar ( 2013 ) defines data science as the study of the generalizable extraction of knowledge from data. At a high level, Provost and Fawcett ( 2013 ) defines data science as a set of fundamental principles that support and guide the principled extraction of information and knowledge from data. Furthermore, Wikipedia defines data science (DS) as extracting useful knowledge from data by employing techniques and theories drawn from many fields within the broad areas of mathematics, statistics, and information technology. The field of statistics is the core building block of DS theory and practice, and many of the techniques for extracting knowledge from data have their roots in this. Traditional statistical analytics mainly have mathematical foundations (Cobb 2015 ); while DS analytics emphasize the computational aspects of pragmatically carrying out data analysis, including acquisition, management, and analysis of a wide variety of data (Hardin et al. 2015 ). More importantly, DS analytics follow frameworks for organizing data-analytic thinking (Baumer 2015 ; Provost and Fawcett 2013 ).

Vision for future education

Character. Disposition. Grit. Growth mindset. Non-cognitive skills. Soft skills. Social and emotional learning. People use these words and phrases to describe skills that they also often refer to as nonacademic skills (Kamenetz 2015 ; Moore et al. 2015 ). Among these various terms, the social-emotional skills promoted by the Collaborative for Academic, Social and Emotional Learning ( http://www.casel.org/ ) have mostly been accepted by the broader educational community (Brackett et al. 2012 ). A growing number of studies show that these nonacademic factors play an important role in shaping student achievement, workplace readiness, and adult well-being (Child Trends 2014 ). For example, Mendez ( 2015 ) finds that nonacademic factors play a prominent role in explaining variation in 15-years-old school children’s’ scholastic performance, as measured by the Program for International Students Assessment (PISA) achievement tests. Lindqvist and Vestman ( 2011 ) also find strong evidence that men who fare poorly in the labor market—in the sense of unemployment or low annual earnings—lack non-cognitive rather than cognitive abilities. Furthermore, Moffitt et al. ( 2011 ) find that the emotional skill of self-control in childhood is associated with better physical health, less substance dependence, better personal finances, and fewer instances of criminal offending in adulthood.

Due to a new understanding of the impact of nonacademic factors in the global economy, a growing movement in education has raised the focus on building social-emotional competencies in national curricula. In fact, countries like China, Finland, Israel, Korea, Singapore, the United States, and the United Kingdom currently mandate that a range of social-emotional skills be part of the standard curriculum (Lipnevich and Roberts 2012 ; Ren 2015 ; Sparks 2016 ). The movement involves some complex issues ranging from the establishment of social and emotional learning standards to the development of social and emotional learning programs for students, and to the offering of professional development programs for teachers, and to the carrying out of social and emotional learning assessments (Kamenetz 2015 ).

However, as argued by Sparks ( 2016 ), research studying these skills has not quite caught up with their growing popularity. A number of authors raise various directions for future research in social and emotional learning. Child Trends ( 2014 ), for instance, conducted a systematic literature review of different social-emotional skills and highlighted the need for further research on the importance of the following five skills: self-control, persistence, mastery orientation, academic self-efficacy, and social competence. Moreover, Moore et al. ( 2015 ) provide conceptual and empirical justification for the inclusion of nonacademic outcome measures in longitudinal education surveys to avoid omitted variable bias, inform the development of new intervention strategies, and support mediating and moderating analyses. Likewise, Levin ( 2013 ) and Sellar ( 2015 ) both suggest that the development of data infrastructure in education should select a few nonacademic skill measures in conjunction with the standard academic performance measures. Furthermore, Duckworth and Yeager ( 2015 ) note that how multidimensional data on personal qualities can inform action in educational practice is another topic that will be increasingly important in this context.

Although all those issues have varying significances regarding the measurement and development of social and emotional learning, the following two research goals are priorities for studies of social and emotional learning:

Developing assessment techniques,

Providing intervention approaches.

These two research areas strongly affect the development of social-emotional skills, which are the principal concerns of the domains of education and data science, and which can be studied to derive evidence-based policies. To consider these issues, this paper focuses on (a) the suggested data science research methodology that is applicable to reach these goals, and (b) the case study of social-emotional learning in which we used the data science research methodology.

Methodology review for data science

To better pursue those goals, it could be useful to formalize the knowledge discovery processes within a standardized framework in DS. There are several objectives to keep in mind when applying a systemic approach (Cios et al. 2007 ): (1) help ensure that the quality of results can contribute to solving the user’s problems; (2) a well-defined DS model should have logical, well-thought-out substeps that can be presented to decision-makers who may have difficulty understanding the techniques behind the model; (3) standardization of the DS model would reduce the amount of extensive background knowledge required for DS, thereby leading directly to a knowledge discovery process that is faster, more reliable, and more manageable.

In the context of DS, the Cross-Industry Standard Process for Data Mining (CRISP-DM) model is the most widely used methodology for knowledge discovery (Guruler and Istanbullu 2014 ; Linan and Perez 2015 ; Shearer 2000 ). It has also been incorporated into commercial knowledge discovery systems, such as SPSS Modeler. To meet the needs of the academic research community, Cios et al. ( 2007 ) further develop a process model based on the CRISP-DM model by providing a more general, research-oriented description of the steps. Applications of Cios et al. process model follow six steps, as shown in Fig.  1 .

Cios et al.’s process model. Source: adapted from Cios and Kurgan ( 2005 )

Understanding of the problem domain

This initial step involves thinking carefully about the use scenario, understanding the problem to be solved and determining the research goals. Working closely with educational experts helps define the fundamental problems. Research goals are structured into one or more DS subtasks, and thus, the initial selection of the DS tools (e.g., classification and estimation) can be performed in the later step of the process. Finally, a description of the problem domain is generated.

An example research goal would be: Since meaningful learning requires motivation to learn, researchers are interested in real-time modeling of students’ motivational orientations (e.g., approach vs. avoidance). Similarly, researchers might be interested in developing models that can automatically detect affective states (e.g., anxiety, frustration, boredom) from machine-readable signals (Huang et al. In Press ; Lai et al. 2016 ; Liu et al. 2015 ).

Understanding of the data

This step includes collecting sample data that are available and deciding which data, including format and size, will be needed. To better understand the strengths and limitations of the data, it also includes checking data completeness, redundancy, missing values, the plausibility of attribute values. Background knowledge can be used to guide these checks. Another critical part of this step is estimating the costs and benefits of each data source and deciding whether further investment in collection is worthwhile. Finally, this step includes verifying that the data matches one or more DS subtasks in the last step.

For example, researchers may decide to analyze log traces in an online learning session to make inferences about students’ motivational orientations. Moreover, researchers may choose to collect physiological data (such as facial expression, blood volume pulse, and skin conductance data) to develop models that can automatically detect affective states.

To date, DS has relied heavily on two data sources (Siemens 2013 ): student information systems (SIS, for in generating learner profiles, such as grade point averages) and learning management systems (LMS). For example, Moodle ( https://moodle.org/ ) and Blackboard ( http://www.blackboard.com/ ) can record logs for user activity in courses, forums, and groups. Linan and Perez ( 2015 ) suggest using Google Analytics to gather information about a site, such as the number of visits, pages visited, the average duration of each visit, and demographics. Massive open online courses (MOOCs) may also provide additional data sets to understand the learning process. For instance, Leony et al. ( 2015 ) show how to infer the learners’ emotions (i.e., boredom, confusion, frustration, and happiness) by analyzing their actions on the Khan Academy Platform. Moreover, a variety of physiological sensors have been used to increase the quality and depth of analysis (Kaklauskas et al. 2015 ), such as wearable technologies (Schaefer et al. 2016 ).

Social computing systems refer to the interplay between people’s social behaviors and their interactions with computing technologies (Cheng et al. 2015 ; Lee and Chen 2013 ). These systems can extract various kinds of behavioral cues and social signals, such as physical appearance, gesture and posture, gaze and face, vocal behavior, and use of space and environment (Zhou et al. 2012 ). Analyzing this information can enable the visually representation of social features, such as identity, reputation, trust, accountability, presence, social role, expertise, knowledge, and ownership (Zhou et al. 2012 ).

There are also open datasets that can be used for research on social and emotional analytics, such as PhysioBank, which includes digital recordings of physiological signals and related data for use by the biomedical research community (Goldberger et al. 2000 ); DEAP, a database for emotion analysis using physiological signals (Koelstra et al. 2012 ); and DECAF, a multimodal dataset for decoding user physiological responses to affective multimedia content (Abadi et al. 2015 ). Verbert et al. ( 2012 ) further review the availability of such open educational datasets, including dataTEL ( http://www.teleurope.eu/pg/pages/view/50630/ ), DataShop ( https://pslcdatashop.web.cmu.edu/ ) and Mulce ( http://mulce.univ-bpclermont.fr:8080/PlateFormeMulce/ ). As highlighted by Siemens ( 2013 ), taking multiple data sources into account provides more information to educators and students than a single data source.

Preparation of the data

This step concerns manipulating and converting the raw data materials into suitable forms that will meet the specific input requirements for the DS tools. For example, some DS techniques are designed for symbolic and categorical data, while others handle only numeric values. Typical examples of manipulation include converting data to different types and discretizing or summarizing data to derive new attributes. Moreover, numerical values must often be normalized or scaled so that they are comparable. Preparation also involves sampling, running correlation and significance tests, and data cleaning, which includes removing or inferring missing values. Feature selection and data reduction algorithms may further be used with the cleaned data. The end results are then usually converted to a tabular format for the next step.

Cios and Kurgan ( 2005 ) demonstrate that the data preparation step is by far the most time-consuming part of the DS process model, but educational DS research rarely examines this. Cristóbal Romero et al. ( 2014 ) survey the literature on pre-processing educational data to provide a guide or tutorial for educators and DS practitioners. Their results showed these seven pre-processing tasks: (1) data gathering, bringing together all the available data into a set of instances; (2) data aggregation/integration, grouping together all the data from different sources; (3) data cleaning, detecting erroneous or irrelevant data and discarding it; (4) user and session identification; identifying individual users; (5) attribute/variable selection, choosing a subset of relevant attributes from all the available attributes; (6) data filtering, selecting a subset of representative data to convert large data sets into smaller data sets; and (7) data transformation, deriving new attributes from the already available ones.

Mining of the data

At this point, various mining techniques are applied to derive knowledge from preprocessed data (see Table  1 ). This usually involves the calibration of the parameters to the optimal values. The output of this step is some model parameters or pattern capturing regularities in the data.

Evaluation of the discovered knowledge

The evaluation stage serves to help ensure that the discovered knowledge satisfies the original research goals before moving on. Only approved models are retained for the next step, otherwise the entire process is revisited to identify which alternative actions could be taken to improve the results (e.g., adjusting the problem definition or getting different data). The researchers will assess the results rigorously and thus gain confidence as to whether or not they are qualified. Scheffel et al. ( 2014 ) conduct brainstorming with experts from the field of learning analytics and gather their ideas about specific quality indicators to evaluate the effects of learning analytics. We summarize the results in Table  2 . The criteria provide a way to standardize the evaluation of learning analytics tools.

In addition, the domain experts will help interpret the results and check whether the discovered knowledge is novel, interesting, and influential. To facilitate their understanding, the research team must think about the comprehensibility of the models to domain experts (and not just to the DS researchers).

As suggested by Romero and Ventura ( 2010 ), visualizing models in compelling ways can make analytics data straightforward for non-specialists to observe and understand. For example, Leony et al. ( 2013 ) propose four categories of visualizations for an intelligent system, including time-based visualizations, context-based visualizations, visualizations of changes in emotion, and visualizations of accumulated information. The main objective of these visualizations is to provide teachers with knowledge about their learner’s emotions, learning causes, and the relationships that learning has with emotions. Verbert et al. ( 2014 ) also review works on capturing and visualizing traces of learning activities as dashboard applications. They present examples to demonstrate how visualization can not only promote awareness, reflection, and sense-making, but also represent learner’s goals and enable them to track progress toward these. Epp and Bull ( 2015 ) explored 21 visual variables (e.g., arrangement, boundary, connectedness, continuity, depth, motion, orientation, position, and shape) that have been employed to communicate a learner’s abilities, knowledge, and interests. Manipulating such visual variables should provide a reasonable starting point from which to visualize educational data.

Use of the discovered knowledge

This final step consists of planning where and how to put the discovered knowledge into real use. A plan can be obtained by simply documenting the action principles being used to impact and improve teaching, learning, administrative adoption, culture, resource allocation and decision making on investment. The discovered knowledge may also be reported in educational systems, where the learner can see the related visualizations. These visualizations can provide learners with information about several factors, including their knowledge, performance, and abilities (Epp and Bull 2015 ). Moreover, the results from the current context may be extended to other cases to assess their robustness. The discovered knowledge is then finally deployed.

However, according to the findings of Romero and Ventura ( 2010 ) survey, only a small minority of studies can apply the discovered knowledge to institutional planning processes. One of the barriers to this is individual and group resistance to innovation and change. Macfadyen and Dawson ( 2012 ) thus highlight that the accessibility and presentation of analytics processes and findings are the keys to motivating participants to feel positive about the change. Furthermore, the initial iteration may not be complete or good enough to deploy, and so a second iteration may be necessary to yield an improved solution. Therefore, the diagram shown in Fig.  1 represents this process as a cycle and describes several explicit feedback loops, rather than as a simple, linear process.

The case of social-emotional learning

In this section, we describe a case study in which we used the data science research methodology. The research was initiated with an instructor who wanted to understand university students’ motivation for learning during a semester. We thus started to help this instructor through understanding the problem (Step 1). The instructor explained that university students’ motivation for learning varies over a long semester. Monitoring their motivation can help in providing the right motivated strategies at the right time. We thus went on to the next step: understanding the data (Step 2). Although the use of the motivated strategies for learning questionnaire (MSLQ) (Garcia and Pintrich 1996 ) can gather data about students’ motivation, the questionnaire measures were quite long and were not sensitive to change over time. Inspired by the concept of teaching opinion survey implemented at the end of a semester, we decided to collect text data to evaluate university students’ motivation to learn. After repeatedly going through Steps 1 and 2, the research problem became “predicting university students’ motivation to learn based on teaching opinion mining.”

In this experiment, we employed the motivated strategies for learning questionnaire to collect the respondents’ motivation states. In addition, an open-ended opinion survey about the challenges they faced on the F2F course and recommendations to the teacher with regard to adjusting instruction was utilized to collect the text data. One hundred and fifty-two university students (62 females, 90 males; mean age ± S.D. = 21.1 ± 7.5 years) completed the survey for this study. They were taking face-to-face computer courses at four universities in southern Taiwan.

In the data preparation step (Step 3), we first calculated the mean score of MSLQ. Those respondents with a score less than the mean were labeled as low motivation (LM) students, while those with more than the mean were labeled as high motivation (HM) students. The sample consisted of 76 LM and 76 HM students (the mean was equal to the median).

We then continued to process the textual data. Because textual data is unstructured, the aim of data preparation is to represent the raw text by numeric values. This process contained two steps: tokenizing and counting. In the tokenizing step, we used the CKIP Chinese word segmentation system (Ma and Chen 2003 ) to handle the text segmentation. In the counting step, term frequency-inverse document frequency (TF-IDF) was used as an indicator parameter to extract text features. TF-IDF is a measure of how frequent a term is in a document, and how rare a term is in many documents.

In mining the data (Step 4), we applied a support vector machine (SVM) to classify the respondents. The dataset was randomly split into two groups: a training set and a testing set. The training set consisted of 138 instances (90%) and the testing set of 14 instances (10%). We constructed a model based on the training set and made predictions on the testing set to evaluate the prediction performance. In the evaluation of the model (Step 5), the rate of correct predictions over all instances was measured to represent the accuracy of the prediction model. Through removing the 1074 stop words and substituting the 39 words having similar meanings, the results revealed that the accuracy of the prediction model could be up to 85.7%. We used a free data analysis software, RapidMiner, to perform the analysis (See Fig.  2 ). Therefore, in the final step the instructor could predict students’ motivation to learn during the whole semester using computer-mediated communication, such as instant messaging (Step 6).

The analysis process in RapidMiner

We further iterated the process by redefining the research problem as “finding groups of respondents using similar terms to describe an opinion.” In mining the data, the K-Means clustering method was used to partition the respondents into two clusters. The cluster model revealed that Cluster 1 had 89 respondents, and Cluster 2 had 63. ANOVA was performed to determine how the score of MSLQ was influenced by participant’s clusters (see Table  3 ). Significant effects across different work methods were found for the two clusters, F (1, 150) = 14.33, p  = .000. Table  3 indicates that the Cluster 2 had a higher mean score of MSLQ than Cluster 1. The cluster model also found that the top three important terms for were “考試(exam)”, “報告(presentation)”, and “作業(homework)” for Cluster 1 and “老師(instructor)”, “同學(peer)”, and “自己(oneself)” for Cluster 2. In other words, the terms used in Cluster 1 concerned more about the value component of MSLQ. However, the terms used in Cluster 2 concerned more about the expectancy component of MSLQ. Therefore, the instructor could use these terms to roughly provide interventions to improve students’ motivation for learning.

The broad availability of data has led to the development of data science. This paper’s research goals are to stimulate further research and practice in the use of data science for education. It also presents a DS research methodology that is applicable to achieve these goals. A well-defined DS research model can help ensure that quality of results, contribute to better understanding the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Through an examination of large data sets, a DS methodology can help us to acquire more knowledge about how people learn (Koedinger et al. 2015 ). This is important, as it contributes to the development of better intervention support for more effective learning.

This paper also describes the emerging field of social-emotional learning and its challenges. It has been proposed that the social-emotional competencies that occur between people will become very important to education in the future. Although research suggests that social-emotional qualities have a positive influence on academic achievement, most related studies examine these qualities in relation to outcome measurement and prediction, and more work is needed to develop interventions based on this research (Levin 2013 ). Therefore, this paper presents a case study of social-emotional learning in which we used the data science research methodology.

Several large problems remain to be addressed by researchers in this field. Before incorporating the approaches recommended in this work in large-scale education settings, we should select a few social-emotional skill areas and measures. This investment in data acquisition and knowledge discovery by DS will enable a deeper understanding of school effects and school policy in this context, and would avoid pulling reform efforts in unproductive or detrimental directions (Whitehurst 2016 ). Moreover, explicit privacy regulations, such as anonymity in data collection and consent from the parents in a K-12 setting, also need to be addressed. Slade and Prinsloo ( 2013 ) recommend collaborating with students on voluntarily providing data and allowing them to access DS outcomes to aid in their learning and development. We hope the issues we have highlighted in this paper help stimulate further research and practice in education.

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This research is partially supported by the Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 105-2511-S-006 -015 -MY2.

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Liu, MC., Huang, YM. The use of data science for education: The case of social-emotional learning. Smart Learn. Environ. 4 , 1 (2017). https://doi.org/10.1186/s40561-016-0040-4

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Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains †

Associated data.

• Appendix 1: Example assessment questions used to assess the effectiveness of case studies at promoting learning
• Appendix 2: Student learning gains were assessed using a modified version of the SALG course evaluation tool

Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

INTRODUCTION

The case study teaching method is a highly adaptable style of teaching that involves problem-based learning and promotes the development of analytical skills ( 8 ). By presenting content in the format of a narrative accompanied by questions and activities that promote group discussion and solving of complex problems, case studies facilitate development of the higher levels of Bloom’s taxonomy of cognitive learning; moving beyond recall of knowledge to analysis, evaluation, and application ( 1 , 9 ). Similarly, case studies facilitate interdisciplinary learning and can be used to highlight connections between specific academic topics and real-world societal issues and applications ( 3 , 9 ). This has been reported to increase student motivation to participate in class activities, which promotes learning and increases performance on assessments ( 7 , 16 , 19 , 23 ). For these reasons, case-based teaching has been widely used in business and medical education for many years ( 4 , 11 , 12 , 14 ). Although case studies were considered a novel method of science education just 20 years ago, the case study teaching method has gained popularity in recent years among an array of scientific disciplines such as biology, chemistry, nursing, and psychology ( 5 – 7 , 9 , 11 , 13 , 15 – 17 , 21 , 22 , 24 ).

Although there is now a substantive and growing body of literature describing how to develop and use case studies in science teaching, current research on the effectiveness of case study teaching at meeting specific learning objectives is of limited scope and depth. Studies have shown that working in groups during completion of case studies significantly improves student perceptions of learning and may increase performance on assessment questions, and that the use of clickers can increase student engagement in case study activities, particularly among non-science majors, women, and freshmen ( 7 , 21 , 22 ). Case study teaching has been shown to improve exam performance in an anatomy and physiology course, increasing the mean score across all exams given in a two-semester sequence from 66% to 73% ( 5 ). Use of case studies was also shown to improve students’ ability to synthesize complex analytical questions about the real-world issues associated with a scientific topic ( 6 ). In a high school chemistry course, it was demonstrated that the case study teaching method produces significant increases in self-reported control of learning, task value, and self-efficacy for learning and performance ( 24 ). This effect on student motivation is important because enhanced motivation for learning activities has been shown to promote student engagement and academic performance ( 19 , 24 ). Additionally, faculty from a number of institutions have reported that using case studies promotes critical thinking, learning, and participation among students, especially in terms of the ability to view an issue from multiple perspectives and to grasp the practical application of core course concepts ( 23 ).

Despite what is known about the effectiveness of case studies in science education, questions remain about the functionality of the case study teaching method at promoting specific learning objectives that are important to many undergraduate biology courses. A recent survey of teachers who use case studies found that the topics most often covered in general biology courses included genetics and heredity, cell structure, cells and energy, chemistry of life, and cell cycle and cancer, suggesting that these topics should be of particular interest in studies that examine the effectiveness of the case study teaching method ( 8 ). However, the existing body of literature lacks direct evidence that the case study method is an effective tool for teaching about this collection of important topics in biology courses. Further, the extent to which case study teaching promotes development of science communication skills and the ability to understand the connections between biological concepts and everyday life has not been examined, yet these are core learning objectives shared by a variety of science courses. Although many instructors have produced case studies for use in their own classrooms, the production of novel case studies is time-consuming and requires skills that not all instructors have perfected. It is therefore important to determine whether case studies published by instructors who are unaffiliated with a particular course can be used effectively and obviate the need for each instructor to develop new case studies for their own courses. The results reported herein indicate that teaching with case studies results in significantly higher performance on examination questions about chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication than that achieved by class discussions and textbook reading for topics of similar complexity. Case studies also increased overall student perceptions of learning gains and perceptions of learning gains specifically related to written and oral communication skills and the ability to grasp connections between scientific topics and their real-world applications. The effectiveness of the case study teaching method at increasing academic performance was not correlated to whether the case study used was authored by the instructor of the course or by an unaffiliated instructor. These findings support increased use of published case studies in the teaching of a variety of biological concepts and learning objectives.

Student population

This study was conducted at Kingsborough Community College, which is part of the City University of New York system, located in Brooklyn, New York. Kingsborough Community College has a diverse population of approximately 19,000 undergraduate students. The student population included in this study was enrolled in the first semester of a two-semester sequence of general (introductory) biology for biology majors during the spring, winter, or summer semester of 2014. A total of 63 students completed the course during this time period; 56 students consented to the inclusion of their data in the study. Of the students included in the study, 23 (41%) were male and 33 (59%) were female; 40 (71%) were registered as college freshmen and 16 (29%) were registered as college sophomores. To normalize participant groups, the same student population pooled from three classes taught by the same instructor was used to assess both experimental and control teaching methods.

Course material

The four biological concepts assessed during this study (chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication) were selected as topics for studying the effectiveness of case study teaching because they were the key concepts addressed by this particular course that were most likely to be taught in a number of other courses, including biology courses for both majors and nonmajors at outside institutions. At the start of this study, relevant existing case studies were freely available from the National Center for Case Study Teaching in Science (NCCSTS) to address mitosis and meiosis and DNA structure and replication, but published case studies that appropriately addressed chemical bonds and osmosis and diffusion were not available. Therefore, original case studies that addressed the latter two topics were produced as part of this study, and case studies produced by unaffiliated instructors and published by the NCCSTS were used to address the former two topics. By the conclusion of this study, all four case studies had been peer-reviewed and accepted for publication by the NCCSTS ( http://sciencecases.lib.buffalo.edu/cs/ ). Four of the remaining core topics covered in this course (macromolecules, photosynthesis, genetic inheritance, and translation) were selected as control lessons to provide control assessment data.

To minimize extraneous variation, control topics and assessments were carefully matched in complexity, format, and number with case studies, and an equal amount of class time was allocated for each case study and the corresponding control lesson. Instruction related to control lessons was delivered using minimal slide-based lectures, with emphasis on textbook reading assignments accompanied by worksheets completed by students in and out of the classroom, and small and large group discussion of key points. Completion of activities and discussion related to all case studies and control topics that were analyzed was conducted in the classroom, with the exception of the take-home portion of the osmosis and diffusion case study.

Data collection and analysis

This study was performed in accordance with a protocol approved by the Kingsborough Community College Human Research Protection Program and the Institutional Review Board (IRB) of the City University of New York (CUNY IRB reference 539938-1; KCC IRB application #: KCC 13-12-126-0138). Assessment scores were collected from regularly scheduled course examinations. For each case study, control questions were included on the same examination that were similar in number, format, point value, and difficulty level, but related to a different topic covered in the course that was of similar complexity. Complexity and difficulty of both case study and control questions were evaluated using experiential data from previous iterations of the course; the Bloom’s taxonomy designation and amount of material covered by each question, as well as the average score on similar questions achieved by students in previous iterations of the course was considered in determining appropriate controls. All assessment questions were scored using a standardized, pre-determined rubric. Student perceptions of learning gains were assessed using a modified version of the Student Assessment of Learning Gains (SALG) course evaluation tool ( http://www.salgsite.org ), distributed in hardcopy and completed anonymously during the last week of the course. Students were presented with a consent form to opt-in to having their data included in the data analysis. After the course had concluded and final course grades had been posted, data from consenting students were pooled in a database and identifying information was removed prior to analysis. Statistical analysis of data was conducted using the Kruskal-Wallis one-way analysis of variance and calculation of the R 2 coefficient of determination.

Teaching with case studies improves performance on learning assessments, independent of case study origin

To evaluate the effectiveness of the case study teaching method at promoting learning, student performance on examination questions related to material covered by case studies was compared with performance on questions that covered material addressed through classroom discussions and textbook reading. The latter questions served as control items; assessment items for each case study were compared with control items that were of similar format, difficulty, and point value ( Appendix 1 ). Each of the four case studies resulted in an increase in examination performance compared with control questions that was statistically significant, with an average difference of 18% ( Fig. 1 ). The mean score on case study-related questions was 73% for the chemical bonds case study, 79% for osmosis and diffusion, 76% for mitosis and meiosis, and 70% for DNA structure and replication ( Fig. 1 ). The mean score for non-case study-related control questions was 60%, 54%, 60%, and 52%, respectively ( Fig. 1 ). In terms of examination performance, no significant difference between case studies produced by the instructor of the course (chemical bonds and osmosis and diffusion) and those produced by unaffiliated instructors (mitosis and meiosis and DNA structure and replication) was indicated by the Kruskal-Wallis one-way analysis of variance. However, the 25% difference between the mean score on questions related to the osmosis and diffusion case study and the mean score on the paired control questions was notably higher than the 13–18% differences observed for the other case studies ( Fig. 1 ).

Case study teaching method increases student performance on examination questions. Mean score on a set of examination questions related to lessons covered by case studies (black bars) and paired control questions of similar format and difficulty about an unrelated topic (white bars). Chemical bonds, n = 54; Osmosis and diffusion, n = 54; Mitosis and meiosis, n = 51; DNA structure and replication, n = 50. Error bars represent the standard error of the mean (SEM). Asterisk indicates p < 0.05.

Case study teaching increases student perception of learning gains related to core course objectives

Student learning gains were assessed using a modified version of the SALG course evaluation tool ( Appendix 2 ). To determine whether completing case studies was more effective at increasing student perceptions of learning gains than completing textbook readings or participating in class discussions, perceptions of student learning gains for each were compared. In response to the question “Overall, how much did each of the following aspects of the class help your learning?” 82% of students responded that case studies helped a “good” or “great” amount, compared with 70% for participating in class discussions and 58% for completing textbook reading; only 4% of students responded that case studies helped a “small amount” or “provided no help,” compared with 2% for class discussions and 22% for textbook reading ( Fig. 2A ). The differences in reported learning gains derived from the use of case studies compared with class discussion and textbook readings were statistically significant, while the difference in learning gains associated with class discussion compared with textbook reading was not statistically significant by a narrow margin ( p = 0.051).

The case study teaching method increases student perceptions of learning gains. Student perceptions of learning gains are indicated by plotting responses to the question “How much did each of the following activities: (A) Help your learning overall? (B) Improve your ability to communicate your knowledge of scientific concepts in writing? (C) Improve your ability to communicate your knowledge of scientific concepts orally? (D) Help you understand the connections between scientific concepts and other aspects of your everyday life?” Reponses are represented as follows: Helped a great amount (black bars); Helped a good amount (dark gray bars); Helped a moderate amount (medium gray bars); Helped a small amount (light gray bars); Provided no help (white bars). Asterisk indicates p < 0.05.

To elucidate the effectiveness of case studies at promoting learning gains related to specific course learning objectives compared with class discussions and textbook reading, students were asked how much each of these methods of content delivery specifically helped improve skills that were integral to fulfilling three main course objectives. When students were asked how much each of the methods helped “improve your ability to communicate knowledge of scientific concepts in writing,” 81% of students responded that case studies help a “good” or “great” amount, compared with 63% for class discussions and 59% for textbook reading; only 6% of students responded that case studies helped a “small amount” or “provided no help,” compared with 8% for class discussions and 21% for textbook reading ( Fig. 2B ). When the same question was posed about the ability to communicate orally, 81% of students responded that case studies help a “good” or “great” amount, compared with 68% for class discussions and 50% for textbook reading, while the respective response rates for helped a “small amount” or “provided no help,” were 4%, 6%, and 25% ( Fig. 2C ). The differences in learning gains associated with both written and oral communication were statistically significant when completion of case studies was compared with either participation in class discussion or completion of textbook readings. Compared with textbook reading, class discussions led to a statistically significant increase in oral but not written communication skills.

Students were then asked how much each of the methods helped them “understand the connections between scientific concepts and other aspects of your everyday life.” A total of 79% of respondents declared that case studies help a “good” or “great” amount, compared with 70% for class discussions and 57% for textbook reading ( Fig. 2D ). Only 4% stated that case studies and class discussions helped a “small amount” or “provided no help,” compared with 21% for textbook reading ( Fig. 2D ). Similar to overall learning gains, the use of case studies significantly increased the ability to understand the relevance of science to everyday life compared with class discussion and textbook readings, while the difference in learning gains associated with participation in class discussion compared with textbook reading was not statistically significant ( p = 0.054).

Student perceptions of learning gains resulting from case study teaching are positively correlated to increased performance on examinations, but independent of case study author

To test the hypothesis that case studies produced specifically for this course by the instructor were more effective at promoting learning gains than topically relevant case studies published by authors not associated with this course, perceptions of learning gains were compared for each of the case studies. For both of the case studies produced by the instructor of the course, 87% of students indicated that the case study provided a “good” or “great” amount of help to their learning, and 2% indicated that the case studies provided “little” or “no” help ( Table 1 ). In comparison, an average of 85% of students indicated that the case studies produced by an unaffiliated instructor provided a “good” or “great” amount of help to their learning, and 4% indicated that the case studies provided “little” or “no” help ( Table 1 ). The instructor-produced case studies yielded both the highest and lowest percentage of students reporting the highest level of learning gains (a “great” amount), while case studies produced by unaffiliated instructors yielded intermediate values. Therefore, it can be concluded that the effectiveness of case studies at promoting learning gains is not significantly affected by whether or not the course instructor authored the case study.

Case studies positively affect student perceptions of learning gains about various biological topics.

Finally, to determine whether performance on examination questions accurately predicts student perceptions of learning gains, mean scores on examination questions related to case studies were compared with reported perceptions of learning gains for those case studies ( Fig. 3 ). The coefficient of determination (R 2 value) was 0.81, indicating a strong, but not definitive, positive correlation between perceptions of learning gains and performance on examinations, suggesting that student perception of learning gains is a valid tool for assessing the effectiveness of case studies ( Fig. 3 ). This correlation was independent of case study author.

Perception of learning gains but not author of case study is positively correlated to score on related examination questions. Percentage of students reporting that each specific case study provided “a great amount of help” to their learning was plotted against the point difference between mean score on examination questions related to that case study and mean score on paired control questions. Positive point differences indicate how much higher the mean scores on case study-related questions were than the mean scores on paired control questions. Black squares represent case studies produced by the instructor of the course; white squares represent case studies produced by unaffiliated instructors. R 2 value indicates the coefficient of determination.

The purpose of this study was to test the hypothesis that teaching with case studies produced by the instructor of a course is more effective at promoting learning gains than using case studies produced by unaffiliated instructors. This study also tested the hypothesis that the case study teaching method is more effective than class discussions and textbook reading at promoting learning gains associated with four of the most commonly taught topics in undergraduate general biology courses: chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. In addition to assessing content-based learning gains, development of written and oral communication skills and the ability to connect scientific topics with real-world applications was also assessed, because these skills were overarching learning objectives of this course, and classroom activities related to both case studies and control lessons were designed to provide opportunities for students to develop these skills. Finally, data were analyzed to determine whether performance on examination questions is positively correlated to student perceptions of learning gains resulting from case study teaching.

Compared with equivalent control questions about topics of similar complexity taught using class discussions and textbook readings, all four case studies produced statistically significant increases in the mean score on examination questions ( Fig. 1 ). This indicates that case studies are more effective than more commonly used, traditional methods of content delivery at promoting learning of a variety of core concepts covered in general biology courses. The average increase in score on each test item was equivalent to nearly two letter grades, which is substantial enough to elevate the average student performance on test items from the unsatisfactory/failing range to the satisfactory/passing range. The finding that there was no statistical difference between case studies in terms of performance on examination questions suggests that case studies are equally effective at promoting learning of disparate topics in biology. The observations that students did not perform significantly less well on the first case study presented (chemical bonds) compared with the other case studies and that performance on examination questions did not progressively increase with each successive case study suggests that the effectiveness of case studies is not directly related to the amount of experience students have using case studies. Furthermore, anecdotal evidence from previous semesters of this course suggests that, of the four topics addressed by cases in this study, DNA structure and function and osmosis and diffusion are the first and second most difficult for students to grasp. The lack of a statistical difference between case studies therefore suggests that the effectiveness of a case study at promoting learning gains is not directly proportional to the difficulty of the concept covered. However, the finding that use of the osmosis and diffusion case study resulted in the greatest increase in examination performance compared with control questions and also produced the highest student perceptions of learning gains is noteworthy and could be attributed to the fact that it was the only case study evaluated that included a hands-on experiment. Because the inclusion of a hands-on kinetic activity may synergistically enhance student engagement and learning and result in an even greater increase in learning gains than case studies that lack this type of activity, it is recommended that case studies that incorporate this type of activity be preferentially utilized.

Student perceptions of learning gains are strongly motivating factors for engagement in the classroom and academic performance, so it is important to assess the effect of any teaching method in this context ( 19 , 24 ). A modified version of the SALG course evaluation tool was used to assess student perceptions of learning gains because it has been previously validated as an efficacious tool ( Appendix 2 ) ( 20 ). Using the SALG tool, case study teaching was demonstrated to significantly increase student perceptions of overall learning gains compared with class discussions and textbook reading ( Fig. 2A ). Case studies were shown to be particularly useful for promoting perceived development of written and oral communication skills and for demonstrating connections between scientific topics and real-world issues and applications ( Figs. 2B–2D ). Further, student perceptions of “great” learning gains positively correlated with increased performance on examination questions, indicating that assessment of learning gains using the SALG tool is both valid and useful in this course setting ( Fig. 3 ). These findings also suggest that case study teaching could be used to increase student motivation and engagement in classroom activities and thus promote learning and performance on assessments. The finding that textbook reading yielded the lowest student perceptions of learning gains was not unexpected, since reading facilitates passive learning while the class discussions and case studies were both designed to promote active learning.

Importantly, there was no statistical difference in student performance on examinations attributed to the two case studies produced by the instructor of the course compared with the two case studies produced by unaffiliated instructors. The average difference between the two instructor-produced case studies and the two case studies published by unaffiliated instructors was only 3% in terms of both the average score on examination questions (76% compared with 73%) and the average increase in score compared with paired control items (14% compared with 17%) ( Fig. 1 ). Even when considering the inherent qualitative differences of course grades, these differences are negligible. Similarly, the effectiveness of case studies at promoting learning gains was not significantly affected by the origin of the case study, as evidenced by similar percentages of students reporting “good” and “great” learning gains regardless of whether the case study was produced by the course instructor or an unaffiliated instructor ( Table 1 ).

The observation that case studies published by unaffiliated instructors are just as effective as those produced by the instructor of a course suggests that instructors can reasonably rely on the use of pre-published case studies relevant to their class rather than investing the considerable time and effort required to produce a novel case study. Case studies covering a wide range of topics in the sciences are available from a number of sources, and many of them are free access. The National Center for Case Study Teaching in Science (NCCSTS) database ( http://sciencecases.lib.buffalo.edu/cs/ ) contains over 500 case studies that are freely available to instructors, and are accompanied by teaching notes that provide logistical advice and additional resources for implementing the case study, as well as a set of assessment questions with a password-protected answer key. Case study repositories are also maintained by BioQUEST Curriculum Consortium ( http://www.bioquest.org/icbl/cases.php ) and the Science Case Network ( http://sciencecasenet.org ); both are available for use by instructors from outside institutions.

It should be noted that all case studies used in this study were rigorously peer-reviewed and accepted for publication by the NCCSTS prior to the completion of this study ( 2 , 10 , 18 , 25 ); the conclusions of this study may not apply to case studies that were not developed in accordance with similar standards. Because case study teaching involves skills such as creative writing and management of dynamic group discussion in a way that is not commonly integrated into many other teaching methods, it is recommended that novice case study teachers seek training or guidance before writing their first case study or implementing the method. The lack of a difference observed in the use of case studies from different sources should be interpreted with some degree of caution since only two sources were represented in this study, and each by only two cases. Furthermore, in an educational setting, quantitative differences in test scores might produce meaningful qualitative differences in course grades even in the absence of a p value that is statistically significant. For example, there is a meaningful qualitative difference between test scores that result in an average grade of C− and test scores that result in an average grade of C+, even if there is no statistically significant difference between the two sets of scores.

In the future, it could be informative to confirm these findings using a larger cohort, by repeating the study at different institutions with different instructors, by evaluating different case studies, and by directly comparing the effectiveness of the case studying teaching method with additional forms of instruction, such as traditional chalkboard and slide-based lecturing, and laboratory-based activities. It may also be informative to examine whether demographic factors such as student age and gender modulate the effectiveness of the case study teaching method, and whether case studies work equally well for non-science majors taking a science course compared with those majoring in the subject. Since the topical material used in this study is often included in other classes in both high school and undergraduate education, such as cell biology, genetics, and chemistry, the conclusions of this study are directly applicable to a broad range of courses. Presently, it is recommended that the use of case studies in teaching undergraduate general biology and other science courses be expanded, especially for the teaching of capacious issues with real-world applications and in classes where development of written and oral communication skills are key objectives. The use of case studies that involve hands-on activities should be emphasized to maximize the benefit of this teaching method. Importantly, instructors can be confident in the use of pre-published case studies to promote learning, as there is no indication that the effectiveness of the case study teaching method is reliant on the production of novel, customized case studies for each course.

SUPPLEMENTAL MATERIALS

Acknowledgments.

This article benefitted from a President’s Faculty Innovation Grant, Kingsborough Community College. The author declares that there are no conflicts of interest.

† Supplemental materials available at http://jmbe.asm.org

12 Digital Transformation Trends & Use Cases in Education in '24

The COVID-19 pandemic has accelerated digital transformation in education as nearly 1.5 billion students across the world became distanced from their classrooms. However, online education is not the only way digital technologies transform the teaching and learning experience. We explore how digital transformation affects the education sector with key technologies and trends.

What does digital transformation mean for education?

Digital transformation in education means digitalizing processes and products to improve the teaching and learning experience for everyone involved.

Digital transformation in education focuses on:

• Accessibility: Digital technologies enable learners (e.g. students, employees) to access learning resources more easily and less expensively than traditional education. People across the world, from all ages, with different socioeconomic statuses have access to classes and resources through the internet. Technologies such as text-to-speech remove the barriers for students with disabilities.
• Interactive learning: Micro lessons, videos, interactive tests, gamification, etc. are all different learning formats that are transforming education with a more interactive learning environment. For example, interactive language teaching apps like Duolingo claim to reach more US learners interested in foreign languages than the school system.
• Customized learning: Computer technology and AI enable educational methods such as adaptive learning where each learner is allowed to learn in a way appropriate to them.

Why is digital transformation in education important now?

School shutdowns and distance education are some of the most profound effects of COVID-19 which has demonstrated the importance and urgency of incorporating digital technologies into education. Even before the pandemic, the education industry was in the process of digital transformation. The image below from research by HolonIQ shows that global EdTech (education technology) venture capital funding had increased from $500 million to $7 billion between 2010 and 2019. The effect of the pandemic is also staggering as the investments almost tripled in 2021.

What are the key technologies and trends enabling digital transformation in education?

1- artificial intelligence.

Artificial intelligence applications can undertake simple but time-consuming tasks in education to ease the workload of educators or school staff. They can also be used to deliver an improved and custom learning experience to students. The applications include:

Improving student performance

• Voice-to-text  technologies transforming classes to notes are helpful to students with hearing impairment
• Text-to-voice technologies help dyslexic students learn more effectively by listening instead of reading.
• Personalized learning  can involve a diverse set of technologies including AI to elicit how a student learns best and tailor the education accordingly. Blended and adaptive learning are examples of methods that combine face-to-face instruction with digital learning tools that encourage students to learn by discovery.

Increasing the effectiveness of staff

• Intelligent FAQ chatbots  to answer questions about class, homework, campus, etc. Chatbots can act as virtual advisors for college students which can free up professors’ time.
• Domain specific chatbots: College admission is a complex and stressful process for high school students. College counsellors have limited time to support hundreds of students. Chatbots focused on the admission process can support students in this challenging and important process
• Educational businesses also have back office functions like finance. Process mining can help identify inefficiencies in the back office functions. Read our article on educational process mining to learn more about the applications of process mining in education.
• Individual automation technologies like RPA or combining multiple automation technologies (also called hyperautomation) can help save the time of support staff.

Explore the top 20 use cases of RPA in education in more detail.

2- Analytics

Digital technologies enable schools to collect and analyze a wealth of data about their students to monitor and enhance their performance. Using traditional and advanced analytics, they can determine where students struggle and succeed, develop new methods, and test whether these methods yield expected results.

3- Augmented reality/Virtual reality

Augmented reality and virtual reality (AR/VR) technologies can create interactive and virtual environments for students and help them better engage with the subject. These technologies can enable virtual field trips to historical locations or facilitate learning-by-doing for applied sciences and medicine. The distance learning experience can also be improved with AR/VR technologies.

4- Internet of Things (IoT)

The increasing use of smartphones and other edge devices improves the connectivity between students and their educational institutions by enabling real-time communication and data transfer. IoT devices can also be used to track young children’s absence or presence in class and alert teachers and parents for their security.

5- Online learning

Distance learning (or remote learning) through Zoom or Skype was an emergency response from schools and colleges to the pandemic. Educational institutions can also build their own online class systems, commonly called learning management systems (LMS), and integrate them into their websites or platforms. This will allow them to customize the online learning experience according to the needs of learners or the subject of the course.

6- Smart classes

Digital technologies have also improved face-to-face learning. Smart classes equipped with smart boards, computers, internet connections, projectors, etc. unlock the ways of delivering learning resources to students that were impossible with a blackboard and chalks.

What are some case studies?

• Google Expedition is an education app that contains 1000 VR and 100 AR tours. It helps teachers and students to explore art galleries, museums, underwater, or outer space. Google is now sunsetting the Expedition app and migrating the tours to Google Arts & Culture and making it available to everyone.

• Arizona State University has leveraged Amazon Echo Dot devices in their campus and student resident halls as voice assistants that provide information about the university for students, faculty, staff, and alumni.
• EdTech company Carnegie Learning provides technology solutions to K-12 schools. Their math learning platform MATHia uses artificial intelligence to act as a personal tutor that adjusts itself continually to each student and delivers a personalized learning experience.

How can educational institutions transform digitally?

We outlined the steps to achieve digital transformation and AI transformation . These steps are similar across industries. These involve understanding the challenges of your business and buying or building solutions to resolve these challenges. When it comes to building custom solutions, working with agencies that have done it before can help.

For more on digital transformation:

• Digital Transformation
• Digital Transformation Statistics
• Digital Transformation Consulting

You can also check our data-driven, sortable/filterable list of digital transformation consultant companies .

If you have more questions about digital transformation or digital education, let us know:

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

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Business process automation: types, use cases & best practices, 3 ways dx improves project management in 2024, in-depth guide to digital transformation culture in 2024.

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15 EdTech research papers that we share all the time

We hope you saw our recent blog post responding to questions we often get about interesting large-scale EdTech initiatives. Another question we are often asked is: “What EdTech research should I know about?” 

As Sara’s blog post explains, one of the Hub’s core spheres of work is research, so we ourselves are very interested in the answer to this question. Katy’s latest blog post explains how the Hub’s research programme is addressing this question through a literature review to create a foundation for further research.  While the literature review is in progress, we thought we would share an initial list of EdTech papers that we often reach for. At the Hub we are fortunate enough to have authors of several papers on this list as members of our team. 

All papers on this list are linked to a record in the EdTech Hub’s growing document library – where you will find the citation and source to the full text. This library is currently an alpha version. This means it’s the first version of the service and we’re testing how it works for you. If you have any feedback or find any issues with our evidence library, please get in touch.

Tablet use in schools: a critical review of the evidence for learning outcomes

This critical review by our own Bjӧrn Haßler, Sara Hennessy, and Louis Major has been cited over 200 times since it was published in 2016. It examines evidence from 23 studies on tablet use at the primary and secondary school levels. It discusses the fragmented nature of the knowledge base and limited rigorous evidence on tablet use in education. 

Haßler, B., Major, L., & Hennessy, S. (2016) Tablet use in schools: a critical review of the evidence for learning outcomes . Journal of Computer Assisted Learning, 32(2), 139-156.

The impact and reach of MOOCs: a developing countries’ perspective

This article challenges the narrative that Massive Open Online Courses (MOOCs) are a solution to low and middle-income countries’ (LMIC) lack of access to education, examining the features of MOOCs from their perspectives. It argues that a complicated set of conditions, including access, language, and computer literacy, among others, challenge the viability of MOOCs as a solution for populations in LMIC. 

Liyanagunawardena, T., Williams, S., & Adams, A. (2013) The impact and reach of MOOCs: a developing countries’ perspective. eLearning Papers , 33(33).

Technology and education – Why it’s crucial to be critical

A thought-provoking read, Selwyn’s book chapter argues that technology and education should continuously be viewed through a critical lens. It points to how the use of technology in education is entwined with issues of inequality, domination, and exploitation, and offers suggestions for how to grapple with these issues. 

Selwyn, N. (2015) Technology and education – Why it’s crucial to be critical. In S. Bulfin, N. F. Johnson & L. Rowan (Eds.), Critical Perspectives on Technology and Education (pp. 245-255). Basingstoke and St. Martins, New York: Palgrave Macmillan.

Moving beyond the predictable failure of Ed-Tech initiatives

This article argues that a narrow vision of digital technology, which ignores the complexity of education, is becoming an obstacle to improvement and transformation of education. Specifically, the authors critically reflect on common approaches to introducing digital technology in education under the guise of promoting equality and digital inclusion.

Sancho-Gil, J.M., Rivera-Vargas, P. & Miño-Puigcercós, R. (2019) Moving beyond the predictable failure of Ed-Tech initiatives. Learning, Media and Technology , early view. DOI: 10.1080/17439884.2019.1666873

Synergies Between the Principles for Digital Development and Four Case Studies

The REAL Centre’s report, which includes contributions from the Hub’s own ranks, is one of the few we’ve seen that provides an in-depth exploration of how the Principles for Digital Development apply to the education sector. It uses four case studies on the work of the Aga Khan Foundation, Camfed, the Punjab Education and Technology Board, and the Varkey Foundation. 

REAL Centre (2018). Synergies Between the Principles for Digital Development and Four Case Studies. Cambridge, UK: Research for Equitable Access and Learning (REAL) Centre, Faculty of Education, University of Cambridge .

Education technology map: guidance document

This report by the Hub’s Jigsaw colleagues accompanies a comprehensive map of 401 resources with evidence on the use of EdTech in low-resource environments. The evidence mapping reviews certain criteria of the resources from sources such as journal indices, online research, evaluation repositories, and resource centres and experts. The type of criteria it maps include: the geographical location of study, outcomes studied, and type of EdTech introduced.  While not inclusive of the latest EdTech research and evidence (from 2016 to the present), this mapping represents a strong starting point to understand what we know about EdTech as well as the characteristics of existing evidence.

Muyoya, C., Brugha, M., Hollow, D. (2016). Education technology map: guidance document. Jigsaw, United Kingdom.

Scaling Access & Impact: Realizing the Power of EdTech

Commissioned by Omidyar Network and written by RTI, this executive summary (with the full report expected soon) is a useful examination of the factors needed to enable, scale, and sustain equitable EdTech on a national basis. Four country reports on Chile, China, Indonesia, and the United States examine at-scale access and use of EdTech across a broad spectrum of students. It also provides a framework for an ecosystem that will allow EdTech to be equitable and able to be scaled.  

S caling Access & Impact: Realizing the Power of EdTech (Executive Summary). Omidyar Network.

Perspectives on Technology, Resources and Learning – Productive Classroom Practices, Effective Teacher Professional Development

If you are interested in how technology can be used in the classroom and to support teacher professional development, this report by the Hub’s Björn Haßler and members of the Faculty of Education at the University of Cambridge emphasizes the key point that technology should be seen as complementary to, rather than as a replacement for, teachers. As the authors put it, “the teacher and teacher education are central for the successful integration of digital technology into the classroom.” The report is also accompanied by a toolkit (linked below) with questions that can be used to interrogate EdTech interventions.

Haßler, B., Major, L., Warwick, P., Watson, S., Hennessy, S., & Nichol, B. (2016). Perspectives on Technology, Resources and Learning – Productive Classroom Practices, Effective Teacher Professional Development . Faculty of Education, University of Cambridge. DOI:10.5281/zenodo.2626440

Haßler, B., Major, L., Warwick, P., Watson, S., Hennessy, S., & Nichol, B. (2016). A short guide on the use of technology in learning: Perspectives and Toolkit for Discussion . Faculty of Education, University of Cambridge. DOI:10.5281/zenodo.2626660

Teacher Factors Influencing Classroom Use of ICT in Sub-Saharan Africa

In this paper, the Hub’s Sara Hennessy and co-authors synthesise literature on teachers’ use of ICT, with a focus on using ICT to improve the quality of teaching and learning. They find evidence to support the integration of ICT into subject learning, instead of treating it as a discrete subject, and to provide relevant preparation to teachers during pre- and in-service training to use ICT in classrooms. Although this evidence has been available for a decade, the implications of the paper’s findings are still not often reflected in practice.  

Hennessy, S., Harrison, D., & Wamakote, L. (2010). Teacher Factors Influencing Classroom Use of ICT in Sub-Saharan Africa. Itupale Online Journal of African Studies, 2, 39- 54.

Information and Communications Technologies in Secondary Education in Sub-Saharan Africa: Policies, Practices, Trends, and Recommendations

This landscape review by Burns and co-authors offers a useful descriptive starting point for understanding technology use in sub-Saharan Africa in secondary education, including the policy environment, key actors, promising practices, challenges, trends, and opportunities. The report includes four case studies on South Africa, Mauritius, Botswana, and Cape Verde. 

Burns, M., Santally, M. I., Halkhoree, R., Sungkur, K. R., Juggurnath, B., Rajabalee, Y. B. (2019) Information and Communications Technologies in Secondary Education in Sub-Saharan Africa: Policies, Practices, Trends, and Recommendations. Mastercard Foundation.

The influence of infrastructure, training, content and communication on the success of NEPAD’S pilot e-Schools in Kenya

This study examines the impact of training teachers to use ICT, on the success of NEPAD’S e-Schools. The e-Schools objectives were to impart ICT skills to students, enhance teachers’ capacities through the use of ICT in teaching, improve school management and increase access to education. Unlike other studies on the subject, Nyawoga, Ocholla, and Mutula crucially recognise that while teachers received technical ICT training, they did not receive training on pedagogies for integrating ICT in teaching and learning. 

Nyagowa, H. O., Ocholla, D. N., & Mutula, S. M. (2014). T he influence of infrastructure, training, content and communication on the success of NEPAD’S pilot e-Schools in Kenya . Information Development, 30(3), 235-246 .

Education in Conflict and Crisis: How Can Technology Make a Difference?

This landscape review identifies ICT projects supporting education in conflict and crisis settings. It finds that most of the projects operate in post-conflict settings and focus on the long-term development of such places. The report hones in on major thematic areas of professional development and student learning. It also presents directions for further research, including considerations of conflict sensitivity and inclusion in the use of ICT. 

Dahya, N. (2016) Education in Conflict and Crisis: How Can Technology Make a Difference? A Landscape Review . GIZ.

Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early-grade reading in Kenya

This randomized controlled trial contributes to the limited evidence base on the effects of different types of ICT investments on learning outcomes. All groups participated in the ‘base’ initiative which focused on training teachers and headteachers in literacy and numeracy, books for every student, teacher guides that matched closely with the content of the students’ book, and modest ICT intervention with tablets provided only for government-funded instructional supervisors. The RCT then compared outcomes from three interventions:  (1) base program plus e-readers for students, (2) base program plus tablets for teachers, and (3) the control group who were treated only with the base program. The paper finds that the classroom-level ICT investments do not improve literacy outcomes significantly more than the base program alone, and that cost considerations are crucial in selecting ICT investments in education.

Piper, B., Zuilkowski, S., Kwayumba, D., & Strigel, C. (2016). Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early-grade reading in Kenya. International Journal of Educational Development (49), 204-214.

[FORTHCOMING] Technology in education in low-income countries: Problem analysis and focus of the EdTech Hub’s work

Informed by the research cited in this list (and much more) – the Hub will soon publish a problem analysis. It will define our focus and the scope of our work. To give a taste of what is to come, the problem analysis will explain why we will prioritise teachers, marginalised groups, and use a systems lens. It will also explore emergent challenges in EdTech research, design, and implementation.

EdTech Hub. (2020). Technology in education in low-income countries: Problem analysis and focus of the Hub’s work (EdTech Hub Working Paper No. 5). London, UK. https://doi.org/10.5281/zenodo.3377829

It is important to note that we have included a mix of research types at varying levels of rigour, from landscape reviews and evidence maps, to critical reviews and case studies. Our list is not comprehensive and has some obvious limitations (they are all in English, for one). If you are interested in exploring more papers and evidence, don’t forget to check out the EdTech Hub’s growing document library , where you will find not just links to the full papers in this list but over 200 resources, with more being added each day.

What interesting EdTech research have you recently read, and what did you take away from it? Let us know in the comments section or on Twitter at @GlobalEdTechHub and use #EdTechHub

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India case study, situation analysis on the effects of and responses to covid-19 on the education sector in asia.

Highlights: 

This case study provides a snapshot of the educational responses and effects of COVID-19 in India and is part of a comprehensive assessment of the effects of and responses to COVID-19 on the Education Sector in Asia. 

The case study considers the direct effects of school closures and reopening and identifies its impact on learners, their families as well as on the overall education system. The objectives of the analysis are:

• to assess and estimate the various impacts of the COVID-19 epidemic on the education sector and stakeholders in India;
• to examine policy and financial implications on progress towards achieving SDG 4-Education 2030; and
• to identify examples of promising responses and strategies in education and associated social sectors, which can be shared with other countries. Finally, the case study presents lessons learned and recommendations for building back better and increasing the resilience of the education system to future shocks.

This case study includes an in-depth thematic deep dive into Community-based Education and how it supported continuity of learning during school closures.

This case study is based on a comprehensive desk-review of qualitative and quantitative evidence and on key informant interviews with relevant education officials, local authorities and teachers.

This situation analysis is a collaboration between UNESCO, UNICEF ROSA and UNICEF EAPRO partly funded by the Global Partnership for Education (GPE) under the UNESCO-UNICEF-World Bank joint project on Global and Regional Response to the COVID-19 Pandemic. 

See all country case studies, sub-regional and regional reports here .

Author/s: UNICEF ROSA, UNICEF EAPRO, UNESCO Bangkok & Cambridge Education

Publication date : October 2021

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Related topics, more to explore, situation analysis - covid-19 and education.

Effects of and responses to COVID-19 on the education sector in Asia

The future of 800 million children across Asia at risk as their education has been severely affected by the COVID-19 pandemic

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Education Sector

Education sector poses to be a core pillar of development in various institutions and plays an integral role in influencing perpetual innovation in the society.

However, the beneficial aspect of education is vested on the mode of system as well as environment deployed. Various educational systems have been introduced and are associated with both merits and disadvantages in facilitating consistency in learning intuitions especially at primary and secondary level. In the United States for instance education institutions are continually embracing year round education system rather than the traditional systems. Year round education system involves operating on 180 days similar to the traditional systems but there are breaks within that period. The system has various modes of plans that have different arrangement of the 180 days as well as the breaks involved.

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Year round education system has been receiving reactions from the learning institutions and education stakeholders in the society. From this perspective this paper wishes to discuss in depth arguments put forward in favor of year round education system. Notably, learning is process and involves various concepts fundamental to higher levels of learning in the society. In this case learning is gradual process that characterized by numerous retrieval of the learnt principles or concepts in order to reflect success in the previous sessions. Therefore retention of information learnt during previous learning situation poses to be paramount importance to the future sessions or level.

From this perceptive the mode of system deployed is instrumental in level of retention of the learnt concepts in the previous sessions. The traditional mode of system that involved a single learning period and a break in summer tends to promote forgiveness in students compared to year round education system (The Editor, Para 4). This is due to the fact that year round education is characterized with numerous breaks within the learning session’s retention rates tends to higher than that involvng a prolonged learning and single vacation during summer. In addition, the human brain is designed to become exhausted after a considerable period of comprehensive concentration and in order recommence high concentration relaxation is crucial. The traditional mode of education system fails to consider this factor but year round education system recognizes the concept through creating short breaks within learning periods. This shows the beneficial aspect of year round education in promoting development of education through enhanced retention of learnt concepts and fostering apt learning environment.

Learning and gasping of concepts varies in students and an intervention is highly recommended in order to improve development in learning of slow students. This can be facilitated through in cooperation of enrichment programs in learning institutions during specific period when schools are not busy. The fact that year round education system involves numerous learning period with breaks in between the learning session time space is established for improvement period (Palmer, , 3). The traditional mode of education system is that has a single break a year after a prolonged learning period is characterized with huge learning materials and deployment of an improvement programs to slow students is challenge. However, in the year round education deployment of such programs is possible due to the numerous breaks whish facilitates sub-division of learning materials into manageable sections (McDonald, 210). Therefore, year round education system is designed to accommodate all students with different learning abilities.

From this perceptive learning in the society is fostered and talents can be monitored and nurtured and provide solutions to various problematic issues in the society. According to research regarding the performance of institutions based of the periods they are open or closed, it was revealed that schools that break for summer register poor results in contrast with those deploying year round education system. Therefore, the system posees to recognize demands in the educations sectors and seals inefficiency loopholes associated with the systems that have long breaks during summers (Marien, 142). The year round education system is divided in various plans that have restructured the 180 days of learning in different patterns. Therefore breaking of learning session varies in institutions due to the various modes of plans embraced. In this case facilitating schedule is easier under year round education than the traditional system where vacations were generally during summers.

The fact under year round education system student went on vacations at different periods an entire region can have a schedule free from congestion. Education operations fostered by year round education promote optimal utilization of resources like classrooms in a learning institution (Ryan, & Cooper, 412). The fact that the system deploys multi-tracking technique an institution ca accommodate extra students at a give time that traditional mode educations system where vacation is once a year during summers. In this case the system plays a significant role in saving resources required to extend learning facilities due to the concept of multi-tracking. Year round education pose to great significance in saving resources of learning institutions swell as promoting orderly in the sectors due to various plans with different vocational periods.

Conclusion Year round education system as modern mode of learning program that is gradually replacing the traditional mode is associated with numerous beneficial traits pose to be the forwards in the sector. The various arguments put forwards in favor of the system are realistic and genuinely pose to have the potential to transform education sector to grater heights and facilitate tremendous achievement of students as well as tutors. In order to register efficiency in education sectors and promote performance in individual student comprehensive endorsement of year round education system is crucial.

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