Predictive learning analytics using deep learning model in MOOCs’ courses videos

Analysis of learning behavior of MOOC enthusiasts has become a posed challenge in the Learning Analytics field, which is especially related to video lecture data, since most learners watch the same online lecture videos. It helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns for learners and predict their performance by MOOC courses video. This paper exploits a temporal sequential classification problem by analyzing video clickstream data and predict learner performance, which is a vital decision-making problem, by addressing their issues and improving the educational process. This paper employs a deep neural network (LSTM) on a set of implicit features extracted from video clickstreams data to predict learners’ weekly performance and enable instructors to set measures for timely intervention. Results show that accuracy rate of the proposed model is 82%–93% throughout course weeks. The proposed LSTM model outperforms baseline ANNs, Super Vector Machine (SVM) and Logistic Regression by an accuracy of 93% in real used courses’ datasets.