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

Author

Listed:
  • Md Rashedul Islam
  • Adiba Mahjabin Nitu
  • Md Abu Marjan
  • Md Palash Uddin
  • Masud Ibn Afjal
  • Md Abdulla Al Mamun

Abstract

Educational Data Mining (EDM) holds promise in uncovering insights from educational data to predict and enhance students’ performance. This paper presents an advanced EDM system tailored for classifying and improving tertiary students’ programming skills. Our approach emphasizes effective feature engineering, appropriate classification techniques, and the integration of Explainable Artificial Intelligence (XAI) to elucidate model decisions. Through rigorous experimentation, including an ablation study and evaluation of six machine learning algorithms, we introduce a novel ensemble method, Stacking-SRDA, which outperforms others in accuracy, precision, recall, f1-score, ROC curve, and McNemar test. Leveraging XAI tools, we provide insights into model interpretability. Additionally, we propose a system for identifying skill gaps in programming among weaker students, offering tailored recommendations for skill enhancement.

Suggested Citation

  • Md Rashedul Islam & Adiba Mahjabin Nitu & Md Abu Marjan & Md Palash Uddin & Masud Ibn Afjal & Md Abdulla Al Mamun, 2024. "Enhancing tertiary students’ programming skills with an explainable Educational Data Mining approach," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-25, September.
    • Handle: RePEc:plo:pone00:0307536
    DOI: 10.1371/journal.pone.0307536
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    References listed on IDEAS

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    1. Souad Larabi-Marie-Sainte & Roohi Jan & Ali Al-Matouq & Sara Alabduhadi, 2021. "The impact of timetable on student’s absences and performance," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    2. Chengxin Yin & Dezhao Tang & Fang Zhang & Qichao Tang & Yang Feng & Zhen He, 2023. "Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-19, October.
    3. Nihal Abuzinadah & Muhammad Umer & Abid Ishaq & Abdullah Al Hejaili & Shtwai Alsubai & Ala’ Abdulmajid Eshmawi & Abdullah Mohamed & Imran Ashraf, 2023. "Role of convolutional features and machine learning for predicting student academic performance from MOODLE data," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-22, November.
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