A Unified Deep Learning Framework for Student Performance Prediction With Multimodal Data in a Blended Learning Environment

Prediction of student performance is crucial for enhancing the quality of higher education worldwide by enabling timely interventions and personalized support. The blended learning environment, which integrates online and offline instruction, has become a predominant paradigm, yet it also brings significant challenges for the prediction of student performance due to the inherent complexity and heterogeneity of multimodal data generated across both environments. Specifically, existing approaches often fail to effectively leverage the synergistic potential of numerical behavioral traces and unstructured textual feedback from instructors. To address this problem, this study proposes a unified deep learning framework that integrates convolutional neural networks (CNN) and bidirectional long short‐term memory (BiLSTM) to capture both temporal dynamics and spatial correlations among features in blended learning environment. Unlike previous studies, we incorporate teacher comments on student assignments as textual inputs alongside traditional numerical features. Extensive experiments based on a real‐world dataset of approximately 14,878 student records from Shandong University of Finance and Economics (SDUFE) demonstrate that the proposed hybrid model outperforms existing approaches in predictive performance. The results also highlight the significant value of leveraging teacher feedback for improving prediction accuracy, offering practical insights for enhancing educational management and supporting student success in blended learning contexts in higher education.