MTL-Frame: An End-to-End Multi-Task Learning Framework for Student Profiling and Composite-Score Estimation

Composite-score estimation (or assessment auditing) and student profiling are two fundamental tasks in Educational Data Mining (EDM). However, existing studies often treat them as separate problems, typically adopting a sequential pipeline in which clustering is first performed and then used for downstream estimation. Such a fragmented paradigm limits the interaction between latent student-group structures and supervised outcome signals, particularly in low-data educational scenarios. To address this limitation, this study proposes “MTL-Frame”, an end-to-end multi-task learning framework that jointly optimizes student profiling and composite-score estimation. MTL-Frame integrates prototype-based contrastive clustering with context-aware regression to directly inject student profile priors into grade estimation. A dispersion regularization and dynamic loss weighting ensure training stability. Experiments conducted on a real-world blended English-course dataset involving 429 university students show that MTL-Frame outperforms representative single-task regression baselines, including XGBoost, Random Forest, SVR, and LSTM, achieving an RMSE of 1.7532, an MAE of 0.8168, and R2 = 0.9812. While this high R2 partly reflects the model’s ability to learn the deterministic scoring aggregation, the performance remains strong even when the final exam score is excluded from the inputs (R2 = 0.9681), confirming genuine estimation capability. Compared to the strongest single-task baseline (XGBoost), MTL-Frame reduces RMSE by 38.2%. The model also obtains a Silhouette Score of 0.3562, indicating its ability to generate meaningful student profiles while maintaining strong estimation accuracy. These results demonstrate that integrating unsupervised profiling priors into supervised estimation can improve model robustness and provide actionable insights for differentiated instructional intervention.