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Enhancing tertiary students' programming skills with an explainable Educational Data Mining approach.

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Summary
This summary is machine-generated.

This study introduces an advanced Educational Data Mining (EDM) system to classify and improve tertiary students' programming skills using machine learning and Explainable AI (XAI). A novel ensemble method, Stacking-SRDA, demonstrated superior performance in skill assessment.

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Area of Science:

  • Educational Data Mining (EDM)
  • Computer Science Education
  • Machine Learning

Background:

  • Educational Data Mining (EDM) offers potential for analyzing student data to predict and improve academic performance.
  • Tertiary education faces challenges in objectively assessing and enhancing students' programming skills.
  • Explainable Artificial Intelligence (XAI) is crucial for understanding the decision-making processes of predictive models in education.

Purpose of the Study:

  • To develop and evaluate an advanced EDM system for classifying and improving tertiary students' programming skills.
  • To integrate effective feature engineering, classification techniques, and XAI for model interpretability.
  • To identify programming skill gaps in underperforming students and provide targeted recommendations.

Main Methods:

  • Feature engineering and selection for programming skill indicators.
  • Evaluation of six machine learning algorithms for classification tasks.
  • Development and validation of a novel ensemble method, Stacking-SRDA.
  • Application of Explainable Artificial Intelligence (XAI) techniques for model transparency.

Main Results:

  • The proposed Stacking-SRDA ensemble method significantly outperformed other evaluated algorithms in accuracy, precision, recall, f1-score, ROC curve, and McNemar test.
  • Rigorous experimentation, including an ablation study, confirmed the effectiveness of the developed approach.
  • XAI tools provided valuable insights into the interpretability of the classification models.
  • The system successfully identified skill gaps and generated tailored recommendations for weaker students.

Conclusions:

  • The advanced EDM system, particularly the Stacking-SRDA ensemble, offers a robust solution for assessing and enhancing tertiary students' programming skills.
  • Integrating XAI enhances trust and understanding of the system's predictions.
  • The skill gap analysis and recommendation system provide a practical tool for educators to support student development.