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

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' CGP A. To achieve this, we reviewed various literature to identify key influencing factors and constructed a initial hypothetical causal graph based on the findings. Additionally, an online survey was conducted, where 1,050 students participated, providing comprehensive data for analysis. Causal analysis validated the relationships among variables, offering deeper insights into their direct and indirect effects on CGP A. Regression models were implemented for CGP A 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%. 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. The education system in Bangladesh, characterized by its highly competitive structure, places substantial emphasis on academic achievements, particularly the Cumulative Grade Point Average (CGP A). In Bangladesh, students are under continuous pressure to achieve a high CGP A, which not only impacts their academic reputation but also has broader implications for their personal and social lives. Failure to maintain a competitive CGP A can lead to severe consequences, such as academic probation or even dropout, which are more common than often realized ( (Nurmalitasari et al., 2023; de Assis et al., 2022)). This system, while striving to maintain high standards, also exposes students to risks related to academic stress and potential burnout, with low CGP A often correlating with decreased motivation and higher dropout rates ((Behr et al., 2020)). Consequently, CGP A holds significant weight in shaping students' academic trajectories, making it an essential factor not only for students themselves but also for educators and institutions aiming to foster positive academic environments. Understanding and accurately predicting CGP A could thus support students in better managing their academic journeys, offering early interventions for those at risk, and allowing educators to tailor their approaches to student needs.