Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Abstract

Learning analytics in MOOCs can be used to predict learner performance, which is critical as higher education is moving towards adaptive learning. Interdisciplinary methods used in the article allow for interpreting empirical qualitative data on performance in specific types of course assignments to predict learner performance and improve the quality of MOOCs. Learning analytics results make it possible to take the most from the data regarding the ways learners engage with information and their level of skills at entry. The article presents the results of applying the proposed learning analytics algorithm to analyze learner performance in specific MOOCs developed by Ural Federal University and offered through the National Open Education Platform.

Keywords

• online learning
• massive open online courses
• learning analytics
• empirical evidence
• assessment tools
• checkpoint assignments
• academic performance monitoring