Analysing student learning pathways in educational systems using CRNN-based multichannel temporal modelling

With the rapid growth of educational data, analysing students’ learning pathways has become increasingly challenging. Traditional learning behaviour analysis methods struggle to handle multi-channel and heterogeneous data, and they often fail to capture complex relationships among student behaviours, particularly in multi-dimensional feature fusion and spatiotemporal dependency modelling. To address these limitations, this paper proposes PathNet-GC, a learning path prediction model that integrates multi-channel temporal feature extraction with graph convolutional networks (GCNs). Specifically, the model first extracts temporal representations from multi-channel learning behaviour data using 1D convolution and Bi-GRU, and then employs a GCN module to model structured relationships among students’ behaviours. Through the deep fusion of temporal features and graph-based behavioural dependencies, PathNet-GC improves the accuracy, stability, and robustness of learning path prediction. Experiments conducted on the EdNet and ASSISTments datasets demonstrate that PathNet-GC outperforms traditional machine learning methods and representative deep learning baselines, especially in terms of F1-score and AUC. Overall, this study not only enhances the effectiveness of learning pathway analysis, but also provides a feasible modelling framework and practical methodological reference for educational data mining, personalized learning support, and intelligent education systems in diverse real-world educational scenarios.