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

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 completio...