用机器学习和因果分析，找出影响学生成绩的关键社会经济因素。
Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis
- 构建假设因果图，结合多种算法挖掘影响成绩的直接与间接因素。
- 岭回归模型MAE为0.12，随机森林分类F1接近完美，预测效果稳定。
- 结果可嵌入网页工具，帮助师生根据数据优化学习策略。
理解影响学生成绩的社会学术与经济因素对于制定有效教育干预措施至关重要。本研究采用多种机器学习技术与因果分析方法，预测并解析这些因素对学业表现的影响。我们构建了假设因果图，并收集了1,050名学生的数据。经过严格的数据清洗与可视化后，通过相关性分析与变量图探索线性关系，并在假设图上进行因果分析。使用回归与分类模型进行预测，同时采用PC、GES、ICA-LiNGAM和GRASP等无监督因果分析算法。回归分析显示，岭回归模型的平均绝对误差（MAE）为0.12，均方误差（MSE）为0.024，表现稳健；分类模型如随机森林达到近乎完美的F1分数。因果分析揭示了出勤率、学习时长和小组学习对加权平均绩点（CGPA）存在显著的直接与间接影响。这些发现通过无监督因果分析得到验证。我们将最优回归模型集成至网页应用中，正开发一个基于实证数据提升学业成果的实用工具。
原文摘要 · Abstract (English)
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.
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