用机器学习预测学生成绩，神经网络表现优于传统模型。
Evaluation of Machine Learning Models in Student Academic Performance Prediction
- 采用多层感知机等模型分析学生行为、学业与人口统计数据。
- 多层感知机测试集最高准确率达86.46%，交叉验证平均79.58%。
- 结合可解释性方法提升模型可信度，适合教育决策参考。
本研究探讨了在校园环境中使用机器学习方法预测学生学业表现的可行性。基于学生的行为、学术及人口统计信息，采用包括多层感知机分类器（MLPC）在内的经典机器学习模型进行实验。在所有实验中，MLPC在测试集上达到最高86.46%的准确率；在10折交叉验证下，测试集平均准确率为79.58%，训练集则为99.65%。相较于其他模型，MLPC表现更优，表明神经网络在数据效率方面具有潜力。特征选择显著提升了模型性能，并通过多种评估方法与现有文献对比。同时，采用可解释性机器学习方法解析黑箱模型，验证了特征选择的有效性。
原文摘要 · Abstract (English)
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach.
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