使用半监督机器学习预测识字干预响应率
Amanda Swee-Ching Tan1, Farhan Ali1, Chiew Lim Lee2
1Learning Sciences and Assessment, National Institute of Education, Nanyang Technological University, Singapore.
Research in developmental disabilities
|August 22, 2025
概括
机器学习模型可以预测特殊教育需求儿童的语音干预成功. 主要预测因素包括语言理解和记忆,有助于量身定制的教育资源分配.
科学领域:
- 教育心理学
- 计算语言学
- 在教育中的机器学习
背景情况:
- 语音干预通常表现出有限的成功,重点是语音技能.
- 预测阅读和拼写等更广泛的识字结果的反应能力至关重要.
- 机器学习有助于更好地预测干预结果.
研究的目的:
- 使用机器学习对系统声学干预反应进行纵向预测.
- 找出成功的关键预测因素在文字阅读和拼写结果.
主要方法:
- 对838名特殊教育需求儿童的数据集应用了12个半监督学习模型.
- 使用了标记 (干预) 和未标记 (无干预) 数据的组合.
- 包括背景,认知和语言成就数据,以及它们的差异,作为预测因素.
主要成果:
- 随机森林和高斯天真贝叶斯模型实现了最高的预测准确性 (F1分数为0.7).
- 结合未标记的数据和扩展的预测集可以提高模型的性能.
- 最重要的预测因素包括语言理解,视觉记忆和语言工作记忆.
结论:
- 确定了声音干预响应的重要预测因素.
- 证明了机器学习在预测教育干预结果方面的价值.
- 这些发现支持更好的资源配置,风险减轻和个性化干预.
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