Decoding student cognitive abilities: a comparative study of explainable AI algorithms in educational data mining

Exploring students’ cognitive abilities has long been an important topic in education. This study employs data-driven artificial intelligence (AI) models supported by explainability algorithms and PSM causal inference to investigate the factors influencing students’ cognitive abilities, and it delve...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 26862 - 20
Hauptverfasser: Niu, Tianyue, Liu, Ting, Luo, Yiming Taclis, Pang, Patrick Cheong-Iao, Huang, Shuaishuai, Xiang, Ao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 24.07.2025
Nature Publishing Group
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Schlagworte:
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Accuracy
Algorithms
Artificial Intelligence
Cognition
Cognition & reasoning
Cognitive abilities
Cognitive ability
Cognitive development
Comparative studies
Critical thinking
Data mining
Data Mining - methods
Education
Educational data mining
Explainability algorithms
Female
Humanities and Social Sciences
Humans
Hypotheses
Machine learning
Male
multidisciplinary
Research methodology
Science
Science (multidisciplinary)
Skills
Socioeconomic factors
Socioeconomic status
Statistical methods
Student teacher relationship
Students
Students - psychology
Educational data mining
Explainability algorithms
Students
Machine learning
Cognitive abilities
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Exploring students’ cognitive abilities has long been an important topic in education. This study employs data-driven artificial intelligence (AI) models supported by explainability algorithms and PSM causal inference to investigate the factors influencing students’ cognitive abilities, and it delved into the differences that arise when using various explainability AI algorithms to analyze educational data mining models. In this paper, five AI models were used to model educational data. Subsequently, four interpretable algorithms, including feature importance, Morris Sensitivity, SHAP, and LIME, were used to globally interpret the results, and PSM causal tests were performed on the factors that affect students’ cognitive abilities. The results reveal that self-perception and parental expectations have a certain impact on students’ cognitive abilities, as indicated by all algorithms. Our work also uncovers that different explainability algorithms exhibit varying preferences and inclinations when interpreting the model, as evidenced by discrepancies in the top ten features highlighted by each algorithm. Morris Sensitivity presents a more balanced perspective, while SHAP and feature importance reflect the diversity of interpretable algorithms, and LIME shows a unique perspective. This detailed observation highlights the practical contribution of interpretable AI algorithms in the field of educational data mining, paving the way for more refined applications and deeper insights in future research.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-12514-5