Predicting student performance in a blended MOOC

• Published in: Journal of computer assisted learning Vol. 34; no. 5; pp. 615 - 628
• Main Authors: Conijn, R., Van den Beemt, A., Cuijpers, P.
• Format: Journal Article
• Language: English
• Published: Oxford Wiley-Blackwell 01.10.2018; Wiley Subscription Services, Inc
• Subjects: Academic Achievement; Academic Failure; Analytics; Behavioral Objectives; Blended Learning; CAI; Computer assisted instruction; Course Evaluation; Data analysis; Distance learning; Grades (Scholastic); Graduate Students; Learning; Learning Analytics; Mathematical analysis; MOOC; MOOC improvement; MOOCs; Online Courses; Outcomes of Education; Performance prediction; predictive modeling; Predictor Variables; process mining; Regression analysis; Students
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.12270

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. Lay Description What is currently known about the subject? Learning analytics focuses on the analysis of learner data to improve learning and teaching. Several studies tried to predict MOOC completion using general frequencies of activities. It is typically found that being active in an MOOC has a positive effect on student performance. It is still hard to translate student performance predictions into actionable feedback. What does this paper add? First step from descriptive learning analytics towards more explanatory learning analytics. It is more insightful to define student performance in MOOCs as obtaining personal learning objective(s). Analysis of order of activities in MOOCs is useful next to frequencies of activities to predict student performance. Students who passed spread their learning over more days compared with students who failed. Analysis of specific course items can be used to identify points of improvement in the MOOC. What does this mean for practitioners? MOOCs can be used for blended learning. Learning analytics on MOOC data can be used for course improvements. Course evaluations are useful to interpret the results from learning analytics.