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Early identification of at-risk students in online higher education is crucial. This study used computational techniques to predict student performance, enabling timely interventions for those likely to fail or succeed.

Keywords:
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Area of Science:

  • Educational Technology
  • Computational Social Science
  • Machine Learning in Education

Background:

  • The rapid shift to online higher education in 2020 led to increased student failure rates.
  • Predicting student performance is vital for developing effective intervention strategies in online learning environments.
  • Computational methods offer potential solutions for early identification of at-risk students.

Purpose of the Study:

  • To build and apply three machine learning classifiers to predict student performance in an online higher education program.
  • To compare the effectiveness of Probabilistic Neural Networks, Support Vector Machines, and Discriminant Analysis for performance prediction.
  • To provide a data-driven approach for identifying students needing early intervention.

Main Methods:

  • Utilized student grade data from an online higher education program across two experiments.
  • Implemented and evaluated three classification models: Probabilistic Neural Network, Support Vector Machine, and Discriminant Analysis.
  • Employed leave-one-out cross-validation and analyzed performance using five distinct criteria, followed by statistical comparison.

Main Results:

  • The study successfully built and applied three classifiers for predicting student performance.
  • Statistical analysis of five performance criteria provided insights into the comparative effectiveness of each model.
  • Results demonstrated the feasibility of timely identification for students at risk of failure or likely to succeed.

Conclusions:

  • The evaluated machine learning models can accurately predict student success and failure in online higher education.
  • Performance criteria analysis aids in selecting the optimal model based on specific prediction objectives.
  • Timely identification facilitates targeted interventions, improving student outcomes in online learning settings.