Implementation of Learning Analytics in MOOC by Using Artificial Unintelligence
Budi Yulianto, Harjanto Prabowo, Raymond Kosala and Manik Hapsara
Journal of Computer Science
Massive Open Online Course (MOOC), a web-based e-learning tool, is growing to be used by current educational institutions. To prevent high non-passing rate, instructor needs to know which learner has the potential to pass the course or not. Learner who will fail the course also need advices immediately from instructor or system to overcome it. Learning Analytics (LA) is needed to collect and analyze learners’ activity logs on MOOC and predict their passing potential. The prototype application is developed by using Rational Unified Process (RUP) software development method. Implementation of LA in MOOC is feasible and suggested to analyze learners’ success factors by consuming learners’ activity logs and visualizing it in scatter diagram and node-link diagram. Instructor can provide advices to learners based on success factors generated by LA.
© 2018 Budi Yulianto, Harjanto Prabowo, Raymond Kosala and Manik Hapsara. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.