土耳其残疾和无残疾学生自决的人口和环境预测因素:基于经典和机器学习的分析
1Nevşehir Hacı Bektaş Veli University, Faculty of Education, Department of Special Education, Nevşehir 50300, Turkey.
Research in developmental disabilities
|January 7, 2026
概括
土耳其学生的自决受个人特征和教育机会的影响. 学校类型和残疾状况成为关键预测因素,突出了支持性实践的必要性.
科学领域:
- 教育心理学教育心理学
- 跨文化心理学 跨文化心理学
- 机器学习在教育中的应用
背景情况:
- 土耳其的教育机会显示出显著的差异.
- 缺乏文化适应的学生自我报告措施阻碍了研究.
- 自决对于学生的发展至关重要.
研究的目的:
- 验证土耳其AIR自决尺度-学生表格.
- 确定自决的上下文预测因素.
- 探索自决能力和机会之间的相互作用.
主要方法:
- 土耳其AIR自决尺度的心理测量验证.
- 经典的统计分析.
- 在大量学生样本上进行机器学习建模.
主要成果:
- 土耳其尺度表现出强大的内部一致性和有效的因素结构.
- 自决得分因残疾,年龄,学校类型,地点和支持而有很大差异.
- 机器学习模型将学校类型和残疾状况确定为主要预测因素,解释了相当大的差异.
结论:
- 自决与个人特征和环境机会有关.
- 建议采用支持自主性的教育实践.
- 数据驱动的策略可以识别和支持机会有限的学生.
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