sis 24(2):

Research Article

Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

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  • @ARTICLE{10.4108/eetsis.4622,
        author={Lijun Pan},
        title={Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={1},
        keywords={Intelligent learning model, Model element extraction, K-means clustering algorithm, Deep compression sparse self-encoder},
        doi={10.4108/eetsis.4622}
    }
    
  • Lijun Pan
    Year: 2024
    Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence
    SIS
    EAI
    DOI: 10.4108/eetsis.4622
Lijun Pan1,*
  • 1: College of Elementary Education Zhengzhou Normal University
*Contact email: cpwater@geegle.com.cn

Abstract

INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial intelligence. OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation indexes. METHODS: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements; then, the intelligent learning model is constructed through a K-means clustering algorithm and deep compression sparse self-encoder; finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment analysis. RESULTS: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient enough. CONCLUSION: The results show that the proposed method improves the model’s accuracy.