在PISA 2018中使用机器学习来预测英国和日本中学生的生活满意度
1Marsal Family School of Education and Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA.
The British journal of educational psychology
|December 21, 2023
概括
机器学习模型可以有效地预测中学学生的生活满意度,识别生活意义和教师支持等关键因素. 这种方法为提高学生福祉和学术成功提供了有价值的见解.
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
- 学生福利研究 学生福利研究
背景情况:
- 学生的生活满意度对于学业成功和长期健康至关重要.
- 之前关于生活满意度的研究很少使用机器学习 (ML) 方法.
- 这项研究通过应用ML来预测学生的生活满意度来弥补这一差距.
研究的目的:
- 使用个人级别的变量通过ML算法来预测中学学生的生活满意度.
- 确定影响学生生活满意度的关键预测因素.
- 探索学习机理在理解学生福祉方面的实用性.
主要方法:
- 利用监督机器学习模型:随机森林 (RF) 和K-最近邻居 (KNN).
- 数据来源于PISA 2018数据集,包括英国和日本的样本.
- 将模型性能进行比较,并确定了重要的预测因素.
主要成果:
- 与日本数据相比,RF和KNN模型在英国数据上显示出更好的预测性能.
- 随机森林模型在预测学生生活满意度方面比KNN模型更准确.
- 确定的主要预测因素包括生活的意义,学生竞争,教师支持,欺凌和ICT资源.
结论:
- 证实了生活满意度的多维性质,并指出了关键的影响因素.
- 率先使用ML技术来调查学生生活满意度的预测因素.
- 为旨在提高中学生生活满意度的干预提供了实际框架.
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