可解释的人工智能用于高等教育中学生压力的预测建模
Rasikh Tariq1, M G Orozco-Del-Castillo2, Muhammad Tayyab Zamir3
1Tecnologico de Monterrey, Institute for the Future of Education, Ave. Eugenio Garza Sada 2501 Sur, Col: Tecnológico, Monterrey, 64700, N.L., Mexico. rasikhtariq@tec.mx.
Scientific reports
|November 3, 2025
概括
这项研究开发了一种具有成本效益的基于调查的机器学习模型,用于检测高等教育中的学生压力. 关键预测因素包括生理和心理社会因素,为学生福祉提供有针对性的机构干预信息.
科学领域:
- 教育心理学教育心理学
- 计算社会科学 计算社会科学
- 医疗信息学 医疗信息学
背景情况:
- 学生的压力是高等教育的一个重要问题.
- 目前的检测方法往往昂贵,缺乏透明度.
- 需要负担得起,可扩展和可解释的压力管理工具.
研究的目的:
- 为大学生开发一个经济有效的,基于调查的压力分类模型.
- 使用多个机器学习算法和可解释的人工智能 (XAI).
- 支持教育机构的透明和可操作的决策.
主要方法:
- 将监督机器学习管道应用于学生调查数据.
- 训练并优化了六个分类算法 (逻辑回归,SVM,决策树,随机森林,梯度提升,XGBoost).
- 使用SHAP分析来分析模型的可解释性和特征重要性排名.
主要成果:
- 随机森林获得了最高的准确性 (0.89),其次是XGBoost (0.87).
- 确定了学生压力的关键预测因素:血压,感知安全,睡眠质量,教师与学生的关系和课外活动参与.
- 生理和心理社会因素都对压力预测有显著的贡献.
结论:
- 基于调查的机器学习方法对于分类学生压力是有效的.
- 针对健康,安全,支持,关系和参与的干预措施可以减轻学生的压力.
- 研究结果支持制定综合政策,以提高高等教育中的学生福祉.
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