The International Review of Research in Open and Distributed Learning just published our article in which we combined Process Mining techniques and Clustering to analyse learning behaviour in MOOCs.
The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students’ activities in a MOOC from the perspective of personal constructivism, which we operationalized as a combination of learning behaviour and learning progress. This study considers students’ data analyzed as per the MOOC Process Mining: Data Science in Action. We explore the relation between learning behaviour and learning progress in MOOCs, with the purpose to gain insight into how passing and failing students distribute their activities differently along the course weeks, rather than predict students’ grades from their activities. Commonly-studied aggregated counts of activities, specific course item counts, and order of activities were examined with cluster analyses, means analyses, and process mining techniques. We found four meaningful clusters of students, each representing specific behaviour ranging from only starting to fully completing the course. Process mining techniques show that successful students exhibit a more steady learning behaviour. However, this behaviour is much more related to actually watching videos than to the timing of activities. The results offer guidance for teachers.
Van den Beemt, A., Buijs, J., & Van der Aalst, W. (2018). Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering. The International Review of Research in Open and Distributed Learning, 19(5). DOI dx.doi.org/10.19173/irrodl.v19i5.3748 (Open Access)
Computers & Education published PhD student’s first article on 360 degree videos in teacher education.
Computer-based classroom simulations have been argued to be a promising way to practice preservice teachers’ (PSTs’) interpersonal competence and to ease the gap between teacher education and educational practice. The systematic literature review presented in this paper examined existing research on the links between PSTs’ interpersonal competence, well-being, and simulations. Furthermore, this review mapped learning experiences, affordances, and hindrances of simulations. Fifteen studies were found eligible for inclusion. Most of these studies reported positive effects of simulations on PSTs’ classroom management and teaching skills in general, rather than specifically on interpersonal competence (e.g., professional interpersonal vision, professional interpersonal knowledge, professional interpersonal repertoire). Concerning PSTs’ well-being, four studies did show positive effects of simulations on PSTs’ self-efficacy. However, none of the studies reported PSTs’ anxiety. Reported affordances were mostly educational (e.g., receiving teacher feedback, available resources) or social (e.g., peer observation, discussions), while the reported hindrances were mainly of a technical nature (e.g., lack of a user-friendly interface, malfunctioning audio or video). Positive learning experiences depended on the degree of realism and authenticity within the simulation. The results of this study provide suggestions for future research on how computer-based simulations in teacher education could contribute to PSTs’ interpersonal competence and well-being.
Theelen, H., Van den Beemt, A., & Den Brok, P. (2019). Classroom simulations in teacher education to support preservice teachers’ interpersonal competence: A systematic literature review. Computers & Education. http://bit.ly/TH1-cae
Predicting student performance is a major tool in learning analytics. This study aims to identify how different measures of massive open online course (MOOC) data can be used to identify points of improvement in MOOCs. In the context of MOOCs, student performance is often defined as course completion. However, students could have other learning objectives than MOOC completion. Therefore, we define student performance as obtaining personal learning objective(s). This study examines a subsample of students in a graduate‐level blended MOOC who shared on‐campus course completion as a learning objective. Aggregated activity frequencies, specific course item frequencies, and order of activities were analysed to predict student performance using correlations, multiple regressions, and process mining. All aggregated MOOC activity frequencies related positively to on‐campus exam grade. However, this relation is less clear when controlling for past performance. In total, 65% of the specific course items showed significant correlations with final exam grade. Students who passed the course spread their learning over more days compared with students who failed. Little difference was found in the order of activities within the MOOC between students who passed and who failed. The results are combined with course evaluations to identify points of improvement within the MOOC.
Conijn, R., Van den Beemt, A., & Cuijpers, P. (2018). Predicting student performance in a blended MOOC. Journal of Computer Assisted Learning, DOI: 10.1111/jcal.12270 (Open Access)
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