特殊教育需求儿童的主观福祉:使用机器学习的纵向预测指标
Amanda Swee-Ching Tan1, Farhan Ali1, Kenneth K Poon2
1Learning Sciences and Assessment, National Institute of Education, Nanyang Technological University, Singapore.
Applied psychology. Health and well-being
|November 12, 2024
概括
机器学习有效地预测了特殊教育需求 (SEN) 的儿童的福祉. 关键因素包括先前的福祉,学术技能和社会互动,揭示了多样化的支持途径.
科学领域:
- 儿童心理学 儿童心理学
- 教育心理学教育心理学
- 机器学习应用 机器学习应用
背景情况:
- 特殊教育需求 (SEN) 的儿童表现出多样化的福祉和心理健康挑战.
- 了解幸福预测因素需要复杂的,多因素的方法.
- 纵向预测模型对于这个人群至关重要.
研究的目的:
- 使用机器学习预测SEN儿童的主观幸福感.
- 识别各种SEN配置文件中幸福感的关键预测因素.
- 探索预测因素和福祉之间的复杂相互作用.
主要方法:
- 对499名患有不同SEN的儿童进行了长度研究 (平均年龄为8.4岁).
- 利用了32个预测变量 (人口统计,生活经验).
- 使用非线性机器学习和经典线性分类器进行预测.
主要成果:
- 非线性机器学习显著优于线性分类器 (F1分数为0.72-0.84).
- 关键预测因素包括先前的福祉,数学的能力,识字能力和人际交往技能.
- 出现了四个不同的集群,突出了不同的预测重要性 (例如",社会化剂"",分析剂").
结论:
- 机器学习揭示了SEN儿童幸福的多种途径.
- 调查结果为支持SEN福祉提供了量身定制的干预措施.
- 人际关系和学术因素至关重要,但它们的影响因个人形象而异.
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