Early Prediction of Student Performance in Face-to-Face Education Environments: A Hybrid Deep Learning Approach With XAI Techniques

A community is only as strong as its weakest link; this principle also applies to student communities in the educational field. The quality of learning achieved by students in a course is directly related to the performance of the weakest student in that course. Therefore, a high number of students failing a course and the necessity of repeating it are undesirable in terms of learning quality. This study aims to predict students' performance early during their coursework to identify those at risk of failing, thus improving the quality of the course and determining the necessary resources to achieve this goal. To this end, we proposed a conceptual model based on a hybrid method combining Particle Swarm Optimization (PSO) and Deep Neural Networks (DNN). To evaluate the classification performance of the model, comparisons were made with classical machine learning and deep learning models. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) methods were used to determine the contribution of different features to the model's predictions. Additionally, to assess the generalizability and applicability of the model, the widely used xAPI-Edu-Data dataset, which covers various courses, was employed, and the accuracy results of the model were compared with early prediction studies published in the literature. As a result, it was found that our prediction model performed 6% better than the classical models and achieved better results than most of the models, except for two models in the literature with similar results. Moreover, important performance features that can be used to evaluate students earlier in the course were identified.