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Data analytics has become one of the emerging tools for the future of education. By analyzing aggregated data from various sources, educators can identify at-risk students who are struggling in their courses and apply a variety of interventions to support their learning. However, despite the abundance of educational data available in universities, accurately identifying students at risk of poor performance in a course remains challenging. In a study conducted at a local university in Hong Kong, 90 students in a senior-year psychology course were involved. We employed a novel data-analytics approach that combined LASSO (Least Absolute Shrinkage and Selection Operator) regression and the Youden index to predict student performance and identify potentially at-risk students in the course. Additionally, we developed an open-source Python package ( https://pypi.org/project/dualPredictor/ ) based on our method. This tool enables educators to easily apply advanced analytics techniques to their datasets, enhancing the accessibility of technology in education. This work underscores the transformative potential of data-driven, learner-centered approaches in higher education.

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A Data-Analytical Framework for the Early Detection of At-Risk Students in Higher Education

  • 章节

  • 发表日期：

    2026年

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  • Chenxi Dong,
  • Jonathan C. Yip,
  • Alpha Man Ho Ling,
  • Joyce Lok Yin Kwan,
  • Philip Leung Ho Yu,
  • Albert Lee,
  • Susanna Siu Sze Yeung,
  • Pamela Pui Wan Leung,
  • Eric Kwan Wai Yu,
  • Eric Chi Keung Cheng,
  • Kwok Tung Tsui,
  • May May Hung Cheng,
  • John Chi-Kin Lee,
  • Wai Keung Li

摘要

Data analytics has become one of the emerging tools for the future of education. By analyzing aggregated data from various sources, educators can identify at-risk students who are struggling in their courses and apply a variety of interventions to support their learning. However, despite the abundance of educational data available in universities, accurately identifying students at risk of poor performance in a course remains challenging. In a study conducted at a local university in Hong Kong, 90 students in a senior-year psychology course were involved. We employed a novel data-analytics approach that combined LASSO (Least Absolute Shrinkage and Selection Operator) regression and the Youden index to predict student performance and identify potentially at-risk students in the course. Additionally, we developed an open-source Python package ( https://pypi.org/project/dualPredictor/ ) based on our method. This tool enables educators to easily apply advanced analytics techniques to their datasets, enhancing the accessibility of technology in education. This work underscores the transformative potential of data-driven, learner-centered approaches in higher education.