Prediction of Learning Progress Using Naive Bayes for The Course Failure Early Warning System Design Ria Arafiyah(a), Meiliasari(a), Ellis Salsabila(a), (b)Alimuddin, Octarina Salsabila(a)
(a) Faculty of Mathematics and Natural Sciences
Universitas Negeri Jakarta
(b) Department of Electrical Engeneering
Universitas Sultan Ageng Tirtayasa
Abstract
Almost in every course, there are some successful learners and unsuccessful learners. There are many methods to reduce the number of unsuccessful learners. One of the methods is to monitor learning progress which can use to recognize whether a learner will pass or not. Monitoring based on modeling learning progress prediction can use in alternative ways. The modeling of learning progress prediction can use as an early warning system (EWS) that can warn the learners throughout the learning process. This study aims to design the Early Warning System (EWS) to monitor learning progress. Decision-making in EWS is a Naive Bayes classification model which develops from the dataset. EWS will detect learner progress according to monitoring and suggest responding to the learner to repair the learning way.
Keywords: Early Warning System (EWS), learning progress, Naive Bayes
