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<p>This study explores the dynamic interplay between faculty online teaching behaviors and student engagement across academic departments, using a longitudinal dataset spanning eight semesters (2020–2024) from 17 departments.Employing multilevel regression analysis and SHAP (SHapley Additive exPlanations) value interpretation, the study identifies teacher feedback behavior as the feature showing the highest relative contribution within the model predictions (β = 0.457, |SHAP| = 0.220, 98.6% bootstrap rank stability) in association with student engagement, revealing substantial variations across departments.The analysis uncovers distinct behavioral patterns: STEM faculty exhibited higher teacher feedback frequencies, whereas humanities faculty emphasized more in-depth feedback.A time-series analysis delineates three developmental stages—adaptation (2020–2021), development (2021–2023), and maturation (2023–2024)—characterized by progressively increasing stability.This study advances educational technology theory by introducing a “dynamic departmental difference” framework and offering empirical support for tailored faculty development strategies in online education.The findings present actionable insights for institutional policymaking in the digital transformation of higher education.</p>

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The dynamic impact of online teaching behavior patterns on student engagement: a longitudinal study based on departmental differences

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Shanwei Lin,
  • Nanhai Li

摘要

This study explores the dynamic interplay between faculty online teaching behaviors and student engagement across academic departments, using a longitudinal dataset spanning eight semesters (2020–2024) from 17 departments.Employing multilevel regression analysis and SHAP (SHapley Additive exPlanations) value interpretation, the study identifies teacher feedback behavior as the feature showing the highest relative contribution within the model predictions (β = 0.457, |SHAP| = 0.220, 98.6% bootstrap rank stability) in association with student engagement, revealing substantial variations across departments.The analysis uncovers distinct behavioral patterns: STEM faculty exhibited higher teacher feedback frequencies, whereas humanities faculty emphasized more in-depth feedback.A time-series analysis delineates three developmental stages—adaptation (2020–2021), development (2021–2023), and maturation (2023–2024)—characterized by progressively increasing stability.This study advances educational technology theory by introducing a “dynamic departmental difference” framework and offering empirical support for tailored faculty development strategies in online education.The findings present actionable insights for institutional policymaking in the digital transformation of higher education.