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- Title
Enhancing tertiary students' programming skills with an explainable Educational Data Mining approach.
- Authors
Islam, Md Rashedul; Nitu, Adiba Mahjabin; Marjan, Md Abu; Uddin, Md Palash; Afjal, Masud Ibn; Mamun, Md Abdulla Al
- 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.
- Subjects
DATA mining; MACHINE learning; ARTIFICIAL intelligence; RECEIVER operating characteristic curves; CLASSIFICATION
- Publication
PLoS ONE, 2024, Vol 19, Issue 9, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0307536