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The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

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  • Tianjiao Wang
  • Xiaona Xia

Abstract

The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based on feature clusters are visualized, the rules of different learning behaviors are summarized, then we propose the practical scheme of interactive cooperation. The hierarchical learning behaviors can be realized by feature clusters, which can effectively improve the modes of interactive cooperation, and help to improve the learning effectiveness.

Suggested Citation

  • Tianjiao Wang & Xiaona Xia, 2023. "The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters," SAGE Open, , vol. 13(2), pages 21582440231, April.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:2:p:21582440231166593
    DOI: 10.1177/21582440231166593
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    References listed on IDEAS

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    1. Fuchs, Sebastian & Di Lascio, F. Marta L. & Durante, Fabrizio, 2021. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Xiaona Xia, 2022. "Application Technology on Collaborative Training of Interactive Learning Activities and Tendency Preference Diversion," SAGE Open, , vol. 12(2), pages 21582440221, April.
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