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

Tianjiao Wang and Xiaona Xia

SAGE Open, 2023, vol. 13, issue 2, 21582440231166593

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.

Keywords: interactive learning environment; feature cluster; hierarchical learning behavior; interactive cooperation; learning analytics (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:2:p:21582440231166593

DOI: 10.1177/21582440231166593

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