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<p>Teacher–student relationships play a vital role in improving college students’ academic performance and the quality of higher education. However, empirical studies with substantial data-driven insights remain limited. To address this gap, this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’ academic performance. A machine learning framework, CQFOA-KELM, was developed by enhancing the Fruit Fly Optimization Algorithm (FOA) with Covariance Matrix Adaptation Evolution Strategy (CMAES) and Quadratic Approximation (QA). CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions. The CQFOA-KELM model achieved an accuracy of 98.15% and a sensitivity of 98.53% in predicting college students’ academic performance. Additionally, it effectively identified the key factors influencing academic performance through the feature selection process.</p>

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Predicting Academic Performance Levels in Higher Education: A Data-Driven Enhanced Fruit Fly Optimizer Kernel Extreme Learning Machine Model

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Zhengfei Ye,
  • Yongli Yang,
  • Yi Chen,
  • Huiling Chen

摘要

Teacher–student relationships play a vital role in improving college students’ academic performance and the quality of higher education. However, empirical studies with substantial data-driven insights remain limited. To address this gap, this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’ academic performance. A machine learning framework, CQFOA-KELM, was developed by enhancing the Fruit Fly Optimization Algorithm (FOA) with Covariance Matrix Adaptation Evolution Strategy (CMAES) and Quadratic Approximation (QA). CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions. The CQFOA-KELM model achieved an accuracy of 98.15% and a sensitivity of 98.53% in predicting college students’ academic performance. Additionally, it effectively identified the key factors influencing academic performance through the feature selection process.