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This paper proposes a model for mining data in blended learning courses implemented via a learning management system (LMS). First, students’ learning behavior data are collected, standardized, and clustered using a combination of Agglomerative Hierarchical Clustering (AHC) and K-Means to identify groups of students with similar learning patterns. Subsequently, from each cluster, linguistic association rules in the form of “IF–THEN” statements are extracted to describe each relationship between the behavioral pattern and the learning outcomes of each group. Experimental results from the “Discrete Mathematics” course at Hanoi National University of Education (HNUE) demonstrate that the proposed model can generate highly interpretable and meaningful rules regarding the relationship between students’ learning behaviors and their learning outcomes.

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Extracting Linguistic Rules from Learning Data Using a Combination of Clustering and Association Rule Mining

  • 会议论文

  • 发表日期：

    2026年

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  • Thi Lan Pham,
  • Thu Hien Doan

摘要

This paper proposes a model for mining data in blended learning courses implemented via a learning management system (LMS). First, students’ learning behavior data are collected, standardized, and clustered using a combination of Agglomerative Hierarchical Clustering (AHC) and K-Means to identify groups of students with similar learning patterns. Subsequently, from each cluster, linguistic association rules in the form of “IF–THEN” statements are extracted to describe each relationship between the behavioral pattern and the learning outcomes of each group. Experimental results from the “Discrete Mathematics” course at Hanoi National University of Education (HNUE) demonstrate that the proposed model can generate highly interpretable and meaningful rules regarding the relationship between students’ learning behaviors and their learning outcomes.