用可解释AI分析影响学生成绩的经济与学术因素，帮学生预测成绩并制定改进策略。
Explainable AI and Machine Learning for Exam-based Student Evaluation: Causal and Predictive Analysis of Socio-academic and Economic Factors
- 构建因果图并结合1050名学生调查数据，分析多维因素对绩点的影响机制。
- 随机森林分类准确率达98.68%，岭回归预测误差低至MAE 0.12、MSE 0.023。
- 通过SHAP等可解释技术识别关键因素，支持个性化学业决策应用。
学业表现受多种社会学术及经济因素共同影响。本研究通过文献综述识别关键变量，构建初始因果图，并开展在线调查，收集1,050名学生的数据进行分析。经过严格的数据清洗与可视化预处理，采用因果分析揭示变量间的直接与间接影响关系。利用回归模型预测绩点，分类模型按表现水平对学生分组：岭回归实现均方误差0.023、平均绝对误差0.12；随机森林分类性能优异，F1-score接近完美，准确率达98.68%。通过SHAP、LIME和Interpret等可解释性AI方法，识别出学习时长、奖学金、父母教育水平和过往成绩为关键影响因素。最终开发基于Web的应用程序，为学生提供个性化成绩预测与改进建议，辅助其做出优化学业决策。
原文摘要 · Abstract (English)
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.
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