article · Applied Sciences
Evaluating student adaptability requires machine learning models that are both accurate and interpretable. Using a dataset of 1,205 records, multiple algorithms were tested to classify student adaptability, with a Random Forest model achieving 91 per cent accuracy alongside high precision, recall, and F1-scores. To explain how predictions are formed, several explainable artificial intelligence techniques were applied, including SHAP, LIME, Anchors, ALE, and counterfactuals. These methods identified class duration and financial condition as major determinants of adaptability, while also revealing the influence of demographic background, institution type, and load-shedding. Counterfactual analysis demonstrated that small shifts in class duration directly alter outcomes. The combined findings suggest that tracking these socioeconomic, infrastructural, and instructional elements can inform tailored educational interventions and broader institutional policy.
Machine learning models used in education often act as black boxes, making it hard to trust their recommendations. Combining high predictive accuracy with explainable artificial intelligence provides educators and policymakers with clear reasons behind classifications. This transparency helps institutions design fair, targeted policies and interventions that address specific barriers such as instructional pacing, socioeconomic hardship, and infrastructure instability.
The work offers an early-stage algorithmic framework that could be integrated into student analytics software or institutional monitoring platforms. Potential users include academic administrators, policymakers, and educational technology developers seeking to build early intervention systems. Because the research remains an applied analytical study tested on a single dataset, practical deployment would require software integration, user interface development, and operational validation in live learning environments.
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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 of 1205 instances, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated highest accuracy at 91%. The models’ precision, recall and F1-score were also evaluated, with Random Forest achieving a precision of 0.93, a recall of 0.94, and an F1-score of 0.94. Our study utilizes SHAP, LIME, Anchors, ALE, and Counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. SHAP values highlighted ‘Class Duration’ significance (mean SHAP value: 0.175); LIME explained socio-economic and institutional factors’ intricate influence. Anchors provided high-confidence rule-based explanations (confidence: 97.32%), emphasizing demographic characteristics. ALE analysis underscored the importance of ‘Financial Condition’ with a positive slope, while Counterfactual scenarios highlighted the impact of slight feature variations of 0.5 change in ‘Class Duration’. 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.
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DOI: 10.3390/app14125141
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