Version 1
: Received: 14 May 2024 / Approved: 14 May 2024 / Online: 14 May 2024 (10:00:45 CEST)
How to cite:
Nnadi, L. C.; Watanobe, Y.; Rahman, M. M.; John-Otumu, A. M. Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models. Preprints2024, 2024050933. https://doi.org/10.20944/preprints202405.0933.v1
Nnadi, L. C.; Watanobe, Y.; Rahman, M. M.; John-Otumu, A. M. Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models. Preprints 2024, 2024050933. https://doi.org/10.20944/preprints202405.0933.v1
Nnadi, L. C.; Watanobe, Y.; Rahman, M. M.; John-Otumu, A. M. Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models. Preprints2024, 2024050933. https://doi.org/10.20944/preprints202405.0933.v1
APA Style
Nnadi, L. C., Watanobe, Y., Rahman, M. M., & John-Otumu, A. M. (2024). Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models. Preprints. https://doi.org/10.20944/preprints202405.0933.v1
Chicago/Turabian Style
Nnadi, L. C., Md. Mostafizer Rahman and Adetokunbo Macgregor John-Otumu. 2024 "Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models" Preprints. https://doi.org/10.20944/preprints202405.0933.v1
Abstract
As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of (XAI) techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated a 91% accuracy. Our study utilizes (SHAP), (LIME), Anchors, (ALE), and counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. Consistently, 'Class Duration' and 'Financial Condition' emerge as key factors, while the study also underscores the subtle effects of 'Institution Type' and 'Load-shedding'. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies.
Keywords
Comparative Analysis; Educational Data Mining; Educational Predictive Modelling; (XAI); Feature Importance; Machine Learning Interpretability; Model Transparency; Predictive Analytics in Education; Student Adaptability; AI in Education Policy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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