The Application of Deep Neural Network in Learning Behavior Prediction of MOOC Learners

Abstract

With the vigorous development of MOOC open online courses, low completion rate of courses has become an important side effect factor which is affecting the promotion and application of MOOC courses in worldwide. Through analysis of edX open data sets, it is found that loss of learners is one of the factors which are leading to low completion rate of MOOC courses, and also there is a close correlation between learning behavior and final scores with loss of learners. Based on this challenge, some research results show that low completion rate of MOOC courses can be greatly improved by predicting the relationship between learning behavior and scores. This article focuses on application of Deep Neural Network (DNN) in prediction and analysis of MOOC learners' learning behavior. First, data analysis of course performance and learning behavior are discussed, then prediction model based on DNN is given for learning behavior and final scores. Second, experiment is done for DNN model, the results show that DNN can be used for analysis and prediction of complex correlation between learning behavior and final scores, thus which can realize more accurate prediction, reduce loss of learner, and carry out individualized teaching intervention to potential learners.