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A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools

Author

Listed:
  • Shan Chen

    (Department of Applied Social Sciences, The Hong Kong Polytechnic University, 11 Yuk Choi Rd., Hung Hom, Hong Kong, China)

  • Yuanzhao Ding

    (School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK)

Abstract

Academic performance prediction is an indispensable task for policymakers. Academic performance is frequently examined using classical statistical software, which can be used to detect logical connections between socioeconomic status and academic performance. These connections, whose accuracy depends on researchers’ experience, determine prediction accuracy. To eliminate the effects of logical relationships on such accuracy, this research used ‘black box’ machine learning models extended with education and socioeconomic data on Pennsylvania to predict academic performance in the state. The decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 48%, 54%, 50%, 51%, and 60%, respectively. The neural network model can be used by policymakers to forecast academic performance, which in turn can aid in the formulation of various policies, such as those regarding funding and teacher selection. Finally, this study demonstrated the feasibility of machine learning as an auxiliary educational decision-making tool for use in the future.

Suggested Citation

  • Shan Chen & Yuanzhao Ding, 2023. "A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools," Social Sciences, MDPI, vol. 12(3), pages 1-13, February.
    • Handle: RePEc:gam:jscscx:v:12:y:2023:i:3:p:118-:d:1079171
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    References listed on IDEAS

    as
    1. Diego Buenaño-Fernández & David Gil & Sergio Luján-Mora, 2019. "Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
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