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As a critical pathway for implementing effective teaching, teacher-student interaction serves as a pivotal breakthrough in improving undergraduate teaching quality. Teachers, acting as facilitators and implementers of classroom interactions, directly determine the efficacy of such interactions through their behavioral patterns. Utilizing survey questionnaire data, this study quantitatively examines the impact of teacher-student interaction on undergraduate students’ learning outcomes in classroom settings by using unsupervised and supervised machine learning approaches. Our results reveal that the orientation, breadth, depth and frequency of teacher-student interaction significantly influence undergraduate learning outcomes.

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Exploring Teacher-Student Interaction-Learning Outcomes Nexus Using Machine Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • Cailian Liu,
  • Jie Wang

摘要

As a critical pathway for implementing effective teaching, teacher-student interaction serves as a pivotal breakthrough in improving undergraduate teaching quality. Teachers, acting as facilitators and implementers of classroom interactions, directly determine the efficacy of such interactions through their behavioral patterns. Utilizing survey questionnaire data, this study quantitatively examines the impact of teacher-student interaction on undergraduate students’ learning outcomes in classroom settings by using unsupervised and supervised machine learning approaches. Our results reveal that the orientation, breadth, depth and frequency of teacher-student interaction significantly influence undergraduate learning outcomes.