标题： 基于考试的学生评估的可解释人工智能和机器学习：社会学术和经济因素的因果与预测分析 显示英文标题

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

摘要： 学术表现取决于社会学术和财务因素的多变量关系。 本研究调查了这些影响因素，以制定优化学生CGPA的有效策略。 为了实现这一目标，我们回顾了各种文献，以确定关键影响因素，并基于研究结果构建了一个初始的假设因果图。 此外，进行了一项在线调查，共有1050名学生参与，提供了用于分析的全面数据。 严格的数据预处理技术，包括清理和可视化，确保在分析前的数据质量。 因果分析验证了变量之间的关系，提供了对其对CGPA的直接和间接影响的更深入了解。 实施了回归模型来进行CGPA预测，而分类模型则根据成绩水平对学生进行分类。 岭回归表现出强大的预测准确性，达到了0.12的平均绝对误差和0.023的均方误差。 随机森林在分类中表现更优，达到了接近完美的F1分数和98.68%的准确率。 可解释的人工智能技术如SHAP、LIME和Interpret增强了模型的可解释性，突出了关键因素，如学习时间、奖学金、父母教育程度和之前的学术表现。 本研究最终开发了一个基于网络的应用程序，为学生提供个性化见解，使他们能够预测学业表现，识别改进领域，并做出有助于提高成果的明智决策。 显示英文摘要

摘要： 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.