Explainable AI and Machine Learning for Exam-based Student Evaluation: Causal and Predictive Analysis of Socio-academic and Economic Factors

TL;DR

This study combines causal and predictive analysis using explainable AI techniques on socio-academic and economic factors to improve student performance evaluation and provide personalized insights through a web application.

Contribution

It introduces an integrated approach of causal analysis, predictive modeling, and explainability in student performance evaluation, supported by extensive survey data and a practical web tool.

Findings

Ridge Regression achieved MAE of 0.12 and MSE of 0.023 in CGPA prediction.

Random Forest classifier attained 98.68% accuracy and near-perfect F1-score.

Explainable AI methods identified key factors like study hours and scholarships affecting CGPA.

Abstract

Academic performance depends on a multivariable nexus of socio-academic and financial factors. This study investigates these influences to develop effective strategies for optimizing students' CGPA. To achieve this, we reviewed various literature to identify key influencing factors and constructed an initial hypothetical causal graph based on the findings. Additionally, an online survey was conducted, where 1,050 students participated, providing comprehensive data for analysis. Rigorous data preprocessing techniques, including cleaning and visualization, ensured data quality before analysis. Causal analysis validated the relationships among variables, offering deeper insights into their direct and indirect effects on CGPA. Regression models were implemented for CGPA prediction, while classification models categorized students based on performance levels. Ridge Regression demonstrated strong predictive accuracy, achieving a Mean Absolute Error of 0.12 and a Mean Squared Error of 0.023. Random Forest outperformed in classification, attaining an F1-score near perfection and an accuracy of 98.68%. Explainable AI techniques such as SHAP, LIME, and Interpret enhanced model interpretability, highlighting critical factors such as study hours, scholarships, parental education, and prior academic performance. The study culminated in the development of a web-based application that provides students with personalized insights, allowing them to predict academic performance, identify areas for improvement, and make informed decisions to enhance their outcomes.