Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8–10, 2023, Wuhan, China

Research Article

Achievement Performance Prediction Model Based on Deep Neural Network of Student Similarity

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  • @INPROCEEDINGS{10.4108/eai.8-9-2023.2340180,
        author={Yixuan  Rong and Tingnian  He and Zhuoran  Li and Guoqi  Liu},
        title={Achievement Performance Prediction Model Based on Deep Neural Network of Student Similarity},
        proceedings={Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8--10, 2023, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICMEIM},
        year={2023},
        month={11},
        keywords={achievement prediction; deep neural networks; dropout},
        doi={10.4108/eai.8-9-2023.2340180}
    }
    
  • Yixuan Rong
    Tingnian He
    Zhuoran Li
    Guoqi Liu
    Year: 2023
    Achievement Performance Prediction Model Based on Deep Neural Network of Student Similarity
    ICMEIM
    EAI
    DOI: 10.4108/eai.8-9-2023.2340180
Yixuan Rong1, Tingnian He1,*, Zhuoran Li1, Guoqi Liu1
  • 1: Northwest Normal University
*Contact email: 87956426@163.com

Abstract

Student performance prediction aims to predict performance through student information and guide students and teachers to optimise the learning process. Traditional methodological studies focus more on students' own influencing factors and ignore the similarity of different students' learning abilities. To address this problem, the performance prediction model based on Deep Neural Network of student similarity (Sim-DNN) takes into account student-to-student associations, and predicts students' academic performance by calculating the similarity of different students' attribute features, selecting students with higher similarity to the target students, and weighting and summing the historical scores of the similar students according to the degree of similarity as the input to the deep neural network . In order to minimise the effect of overfitting, the model incorporates Dropout in the DNN.The experimental results show that the model proposed in this paper has a better prediction performance on both public datasets, Mathematics and Portuguese.