This study proposes a Deep Learning (DL) framework based on a Multilayer Perceptron (MLP) to predict student performance in personalized English language learning environments. The dataset comprises academic features (test scores and previous assignment scores), behavioral features (engagement rate and time spent on the learning platform), and temporal statistical features, including rolling mean, standard deviation, and performance improvement rate. Data preprocessing techniques such as missing value imputation, normalization, and feature engineering are applied to ensure data quality and consistency. Recursive Feature Elimination (RFE) is employed to identify the most influential features, reducing training complexity while improving predictive performance. The MLP model is trained for 100 epochs and evaluated using standard regression metrics. Experimental results demonstrate excellent predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.0937, Root Mean Square Error (RMSE) of 0.6542, and an R² score of 0.9916. Residual analysis indicates errors are centered around zero with no significant systematic bias, while close convergence between training and validation errors suggests strong generalization and minimal overfitting. Furthermore, actual-versus-predicted comparisons show a high degree of agreement, confirming the model’s reliability. The proposed framework enables timely identification of at-risk learners and supports personalized educational interventions. The novelty of this research lies in integrating academic, behavioral, and temporal features within an MLP-based decision-support framework for student performance prediction in language education.
Linjuan Cao (Wed,) studied this question.