Predicting Students’ Cognitive Profiles Using Explainable Machine Learning

Conventional educational strategies fail to comprehend and leverage the diversity of learners’ cognitive strengths and overlook their innate intelligence, a fundamental driver of learning. To address this gap, this study proposes a machine learning (ML) framework to predict students’ overall innate intelligence scores, independent of subject domain or exam structure, using the Learning Meta-Learning dataset, which includes data from 1,021 university students. Seven regression models, including Decision Tree, Random Forest, Extra Trees, Gradient Boosting, Extreme Gradient Boosting, LightGBM, and CatBoost, along with their ensembles have been trained and evaluated. Explainable Artificial intelligence (XAI) technique SHAP is used for important feature selection among 54 features and recursive feature elimination to further enhance model accuracy and interpretability. In comparison to the conventional method, the proposed SHAP-based ML approach is lightweight, trained with selected features, and has shown improvements in accuracy. The accuracy without XAI on CatBoost is 98.32%, whereas with XAI on CatBoost it is 98.53% using only 35 features out of 54. These findings suggest that integrating learners’ cognitive profile prediction model can aid the design of personalized educational strategies, moving beyond one-size-fits-all educational strategies.