Early Prediction of Student Academic Outcomes Using a Mixture of Experts Network

ABSTRACT Predicting learning outcomes is an important problem in educational research, as it enables timely intervention for learners and supports educators in implementing personalized strategies to improve training quality. This task is especially important in online and blended learning environments, where students must independently manage their learning progress. Deep learning (DL) models, particularly Long Short‐Term Memory (LSTM) networks, have shown promise in improving prediction accuracy with time‐series data. However, single‐network models often face high computational costs and struggle to capture the diversity of learner behaviors, resulting in limited performance. To address this, we propose a novel architecture called MoELA to solve the pass/fail classification problem, based on the Mixture of Experts (MoE) framework, which uses multiple expert networks to flexibly model different behavior groups. Each expert includes an LSTM layer integrated with an attention mechanism to effectively process time‐series data and emphasize critical learning moments. The number of experts is determined by clustering learner behavior using the Fuzzy C‐Means (FCM) algorithm. Experiments were conducted on the Open University Learning Analytics Dataset (OULAD). To mitigate class imbalance in the Pass/Fail prediction task, we introduced SMOTERN, a data balancing method that combines SMOTE with noise removal. Results show that MoELA models significantly outperform the traditional four‐layer stacked LSTM. The best performance was achieved by the MoELA4 model trained on SMOTERN‐balanced data, with Accuracy, AUC, and F1 scores of 0.9238, 0.9568, and 0.8637, respectively. Additionally, the proposed architecture requires fewer parameters and achieves faster training and prediction times compared to the stacked LSTM model.