Student Academic Success Prediction Using Learning Management Multimedia Data With Convoluted Features and Ensemble Model

Abstract

Predicting students’ academic success is crucial for educational institutions to provide targeted support and interventions to those at risk of underperforming. With the increasing adoption of digital learning management systems (LMS), there has been a surge in multimedia data, opening new avenues for predictive analytics in education. Anticipating students’ academic performance can function as an early alert system for those facing potential failure, enabling educational institutions to implement interventions proactively. This study proposes leveraging features extracted from a convolutional neural network (CNN) in conjunction with machine learning models to enhance predictive accuracy. This approach obviates the need for manual feature extraction and yields superior outcomes compared to using machine learning and deep learning models independently. Initially, nine machine learning models are applied to both the original and convoluted features. The top-performing individual models are then combined into an ensemble model. This research work makes an ensemble of support vector machine (SVM) and random forest (RF) for academic performance prediction. The efficacy of the proposed method is validated against existing models, demonstrating its superior performance. With an accuracy of 97.88%, and precision, recall, and F1 scores of 98%, the proposed approach attains outstanding results in forecasting student academic success. This study contributes to the burgeoning field of predictive analytics in education by showcasing the effectiveness of leveraging multimedia data from learning management systems with convoluted features and ensemble modeling techniques.