Analysing Structured Learning Behaviour in MOOCs

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.

Abstract

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)

 

Classroom simulations in teacher education to support pre-service teachers’ interpersonal competence

Computers & Education published PhD student’s first article on 360 degree videos in teacher education.

Abstract

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