A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction

Abstract

Educational institutions are utilizing Deep Learning (DL) techniques to develop predictive systems that identify students at risk of underperforming based on historical academic data patterns, thereby enhancing their educational outcomes through targeted interventions. From this outlook, an Ensemble Generative Adversarial Network with a Student Accomplishment prediction using the Distinctive DL (EGAN-SADDL) model was designed to generate large-scale student data and predict their academic achievements. However, integrating heterogeneous kinds of student data into the SADDL model is a complex task that, if not executed properly, may result in the model failing to capture crucial data relationships, leading to lower performance. Hence, this paper proposes an EGAN with Improved SADDL (EGAN-ISADDL) model based on multi-view learning for predicting student academic performance. The main aim of this model is to learn features from multiple sources, including academic records, demographic information, and social media activity, using the multi-view learning approach. First, the academic and demographic attributes of students are collected, along with the physiological features extracted from the information posted on social media by students. Second, the Long Short-Term Memory with Deep Convolutional Neural Network (LSTM-DCNN) and Recursive Neural Network (ReNN) models receive these features in parallel, extracting intermediate features in multiple views. Third, a multi-view classifier jointly learns each set of features to predict students' academic performance, enabling early identification of at-risk students with high accuracy. Finally, experiments conducted on a dataset of 50,000 student records demonstrate that EGAN-ISADDL attains 96.28% accuracy compared to the existing single-view learning models.