基于机器学习方法,构建高中学生负面学术情绪的预测模型
Shumeng Ma1, Ning Jia2, Xiuchao Wei1,3
1College of Education, Hebei Normal University, Shijiazhuang, 050024, China.
Scientific reports
|June 1, 2025
概括
机器学习准确地预测高中学生的负面学术情绪. 关键因素包括情绪控制和归因风格,为心理健康干预提供了洞察力.
科学领域:
- 教育心理学教育心理学
- 机器学习应用 机器学习应用
- 青少年心理健康 青少年心理健康
背景情况:
- 负面的学术情绪会影响学生的福祉和学习.
- 生态系统理论为理解影响因素提供了一个框架.
- 在高中生中促进心理健康至关重要.
研究的目的:
- 评估机器学习算法来预测高中学生的负面学术情绪.
- 确定导致这些情绪的关键个人和学校因素.
- 为提高学生心理健康的干预措施提供信息.
主要方法:
- 研究对象是来自中国河北省的1710名高中生.
- 收集的数据包括弹性,归因风格,自我效能,教师纪律和学术情绪.
- 机器学习模型 (如随机森林,物流回归) 用于预测和因素分析.
主要成果:
- 随机森林模型实现了最高的预测准确率,为83.9%.
- 预期特征选择确定影响控制是最重要的预测因素.
- 其他关键因素包括能力归因,运气归因,背景归因和自我有效性.
结论:
- 机器学习模型是预测负面学术情绪的有效工具.
- 干预策略应侧重于培养情绪控制和归因风格.
- 研究结果为教育实践提供了有价值的见解,以支持学生的心理健康.
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