Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis

TL;DR

This study combines machine learning and causal analysis to identify and predict socio-economic factors affecting student performance, providing insights and a practical tool for educational improvement.

Contribution

It introduces a comprehensive approach integrating causal graphs, multiple ML models, and unsupervised causality algorithms to analyze student performance factors.

Findings

Ridge Regression achieved MAE of 0.12 and MSE of 0.024.

Random Forest classifier achieved nearly perfect F1-score.

Causal analysis identified key factors like attendance and study hours affecting CGPA.

Abstract

Understanding socio-academic and economic factors influencing students' performance is crucial for effective educational interventions. This study employs several machine learning techniques and causal analysis to predict and elucidate the impacts of these factors on academic performance. We constructed a hypothetical causal graph and collected data from 1,050 student profiles. Following meticulous data cleaning and visualization, we analyze linear relationships through correlation and variable plots, and perform causal analysis on the hypothetical graph. Regression and classification models are applied for prediction, and unsupervised causality analysis using PC, GES, ICA-LiNGAM, and GRASP algorithms is conducted. Our regression analysis shows that Ridge Regression achieve a Mean Absolute Error (MAE) of 0.12 and a Mean Squared Error (MSE) of 0.024, indicating robustness, while classification models like Random Forest achieve nearly perfect F1-scores. The causal analysis shows significant direct and indirect effects of factors such as class attendance, study hours, and group study on CGPA. These insights are validated through unsupervised causality analysis. By integrating the best regression model into a web application, we are developing a practical tool for students and educators to enhance academic outcomes based on empirical evidence.