Interpretable early warning recommendations in interactive learning environments: a deep-neural network approach based on learning behavior knowledge graph

Early warning recommendation is crucial for tracking learning behavior and represents a significant issue in interactive learning environments. However, an interactive learning environment-based learning process may not always achieve expected goals, leading to inefficient or ineffective learning behavior and negative emotions. Additionally, many learners fail assessments due to these issues. To address this problem, this study proposes relevant test problems for interpretable early warning recommendations based on massive learning behavior instances and potential relationships. We design an applicable learning analysis model, namely a deep-neural network based on the knowledge graph of learning behavior, and verify its feasibility and reliability through extensive experiments and data analysis. Our results demonstrate that the interactive learning process must match multi-factor analysis at different temporal sequences to determine key temporal sequences or intervals. This is limited by the classification of learning contents and interpretable concepts, which provide effective reference for subsequent learning content with similar concept classes and knowledge structures. Our approach recommends effective learning behavior in appropriate temporal sequences as soon as possible or constructs feasible intervention measures to improve learners' participation. This research deepens and expands early warning by proposing a feasible new method and obtaining key conclusions with vital practical significance.