Discovering learning behavior patterns to predict dropout in MOOC

Abstract

High dropout rate of MOOC is criticized while a dramatically increasing number of learners are appealed to these online learning platforms. Various works have been done on analysis and prediction of dropout. Machine learning techniques are widely applied to this field. However, a single classifier may not always perform reliable for predictions. In this work, we study dropout prediction for MOOC. A technique is proposed to predict dropouts using learning activity information of learners. We applied a two-layer cascading classifier with a combination of three different machine learning classifiers — Random Forest (RF), Support Vector Machine (SVM), and MultiNomial Logistic Regression (MLR) for prediction. Experimental results indicate that the technique is promising in predicting dropouts with achieving 97% precision.