Student Performance Prediction Using Virtual Learning Environment (VLE) Interactions

Abstract

Technological advancements have reshaped the education landscape through the introduction of digital learning platforms. Although higher education institutions are striving to increase the learning outcome and reduce the dropout rates, they still face challenges. Virtual Learning Environments (VLEs) have become essential platforms for delivering instructional content and assessing student engagement. This study aims to predict the students' learning outcomes for the database management subject using VLE log data. This study utilised 78,175 VLE click events generated by two hundred and fortyseven (247) students in a distance learning environment from a state university in Sri Lanka. The study utilised seven behavioural features, number of unique components, average hour, standard deviation of the hour, average number of days, number of weekend interactions, number of session count, peak study hour and thirty-four learning activity features to predict the learning outcome. From the Exploratory Factor Analysis (EFA) session count, the number of weekend interactions, and the unique components are selected as the most influential behavioural features, grade user report viewed, discussion created, discussion viewed, course viewed, a file has been uploaded, feedback viewed and course module viewed have been selected as the most influential learning activity features. The study utilises traditional Machine Learning approaches such as Random Forest Regressor, Support Vector Machines (SVM), and Deep Learning approaches, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) to perform the prediction. Among this approaches the Long Short-Term Memory (LSTM) model, a type of RNN outperform other approaches in terms of accuracy, Mean Absolute Error (MAE), and F1 score. The LSTM model achieved 97% accuracy.