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Introduction <p>Unlike variable-centered analysis, person-centered analysis facilitates the development of more personalized learning. In this study, we investigated the predictors of academic performance in pre-clinical education using variable-centered analysis and identified academic performance profiles using person-centered analysis (cluster analysis).</p> Methods <p>A total of 321&#xa0;second-year medical students from our medical English course at Hokkaido University participated in this study between 2019 and 2021. For variable-centered analysis, we assessed the predictors of academic performance (final exam score) using multivariable regression analysis. We performed cluster analysis using Ward’s minimum variance hierarchical clustering method for person-centered analysis. Nine variables, identical to those used in the variable-centered analysis, were selected and standardized for this analysis.</p> Results <p>Online education, female sex, baseline reading skills, and medical terminology predicted academic performance in the variable-centered analysis; however, cluster analysis identified four subgroups with different academic performances. Cluster 1 had the highest proportion of male and in-person education and the lowest academic performance. Cluster 2, with a high male ratio and low baseline skills, had poor academic performance, whereas most students had online/combined education. Clusters 3 and 4 had the highest online/combined education (Cluster 4: online&gt; combined), and female students with the highest performance.</p> Discussion <p>Online education was the main predictor of academic performance using traditional analysis; however, cluster analysis captured a subgroup of students with online education but lacking better academic performance. The current person-centered analysis contributes novel insights into the nuanced interplay of educational formats, gender, and skills, offering implications for optimizing medical education strategies in the future.</p>

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Clustering of students in pre-clinical medical education and identification of academic performance profiles: does online education work for all medical students?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Houman Goudarzi,
  • Masahiro Onozawa,
  • Yoichi M. Ito,
  • Makoto Takahashi

摘要

Introduction

Unlike variable-centered analysis, person-centered analysis facilitates the development of more personalized learning. In this study, we investigated the predictors of academic performance in pre-clinical education using variable-centered analysis and identified academic performance profiles using person-centered analysis (cluster analysis).

Methods

A total of 321 second-year medical students from our medical English course at Hokkaido University participated in this study between 2019 and 2021. For variable-centered analysis, we assessed the predictors of academic performance (final exam score) using multivariable regression analysis. We performed cluster analysis using Ward’s minimum variance hierarchical clustering method for person-centered analysis. Nine variables, identical to those used in the variable-centered analysis, were selected and standardized for this analysis.

Results

Online education, female sex, baseline reading skills, and medical terminology predicted academic performance in the variable-centered analysis; however, cluster analysis identified four subgroups with different academic performances. Cluster 1 had the highest proportion of male and in-person education and the lowest academic performance. Cluster 2, with a high male ratio and low baseline skills, had poor academic performance, whereas most students had online/combined education. Clusters 3 and 4 had the highest online/combined education (Cluster 4: online> combined), and female students with the highest performance.

Discussion

Online education was the main predictor of academic performance using traditional analysis; however, cluster analysis captured a subgroup of students with online education but lacking better academic performance. The current person-centered analysis contributes novel insights into the nuanced interplay of educational formats, gender, and skills, offering implications for optimizing medical education strategies in the future.