Proceedings of the 3rd International Conference on New Media Development and Modernized Education, NMDME 2023, October 13–15, 2023, Xi’an, China

Research Article

Graph Knowledge Tracking Interaction Model Combining Classification and Regression Tree

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  • @INPROCEEDINGS{10.4108/eai.13-10-2023.2341299,
        author={Zhuoran  Li and Tingnian  He and Yixuan  Rong and Guoqi  Liu},
        title={Graph Knowledge Tracking Interaction Model Combining Classification and Regression Tree},
        proceedings={Proceedings of the 3rd International Conference on New Media Development and Modernized Education, NMDME 2023, October 13--15, 2023, Xi’an, China},
        publisher={EAI},
        proceedings_a={NMDME},
        year={2024},
        month={1},
        keywords={knowledge tracing graph neural networks deep learning decision trees},
        doi={10.4108/eai.13-10-2023.2341299}
    }
    
  • Zhuoran Li
    Tingnian He
    Yixuan Rong
    Guoqi Liu
    Year: 2024
    Graph Knowledge Tracking Interaction Model Combining Classification and Regression Tree
    NMDME
    EAI
    DOI: 10.4108/eai.13-10-2023.2341299
Zhuoran Li1, Tingnian He1,*, Yixuan Rong1, Guoqi Liu1
  • 1: Northwest Normal University
*Contact email: hetn@nwnu.edu.cn

Abstract

Knowledge tracking aims to assess learners' current level of knowledge based on their previous response performance. In recent years, graph neural networks have been successfully applied in the field of knowledge tracking. However, existing graph-based knowledge tracking interaction models typically combine a graph convolutional neural network with a Long Short-Term Memory network. Although there is an improvement in performance, the model only considers exercises, knowledge, and answers as inputs, ignoring the impact of rich learning behavioral features on the learner's knowledge state. In this work, we propose a Graph-based Interaction Model for Knowledge Tracing with Decision Tree (GIKT-DT) that fuses classification and regression trees. Especially, to effectively capture the effect of behavioral features on answer results, predicted responses are first obtained by pre-processing learners' behavioral features using classification and regression trees. And then the cross-sectional features of predicted responses and interaction sequences are calculated as inputs to the GIKT-DT to track learners' knowledge acquisition levels more accurately. Moreover, we validate the GIKT-DT model on three publicly available online education datasets. The experimental results show that GIKT-DT outperforms other baseline models and can better utilize the behavioral characteristics of learners to improve the accuracy and effectiveness of knowledge tracking with better prediction performance.