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

Md Rashedul Islam, Adiba Mahjabin Nitu, Md Abu Marjan, Md Palash Uddin, Masud Ibn Afjal and Md Abdulla Al Mamun

PLOS ONE, 2024, vol. 19, issue 9, 1-25

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.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0307536 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 07536&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0307536

DOI: 10.1371/journal.pone.0307536

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().