数学流和数字处理技能在识别学生的数学学习障碍
H Hellstrand1, S Holopainen2, J Korhonen1
1Faculty of Education and Welfare Studies, Åbo Akademi University, Vaasa, Finland.
Research in developmental disabilities
|June 26, 2024
概括
数字处理技能准确地识别了数学学习障碍 (MLD) 和低成绩 (LA) 的学生. 这项研究强调评估数字技能和算术流性,以便在学生中有效识别MLD.
科学领域:
- 教育心理学教育心理学
- 学习的神经科学学习的神经科学.
- 发展心理学 发展心理学
背景情况:
- 数学学习障碍 (MLD) 的学生在数字处理和算术流方面表现出缺陷.
- 目前的MLD识别主要依赖于算术流性,可能忽视关键的数字处理技能差异.
- 数字处理技能被认为是MLD和低成就 (LA) 之间的关键差异化因素.
研究的目的:
- 评估数字处理技能在识别学生的MLD和LA时的准确性.
- 确定数字处理技能的分类准确性是否在各年级 (3-9年级) 之间有所不同.
- 为了比较数处理技能对MLD和LA的预测能力.
主要方法:
- 利用了18405名学生 (3-9年级) 的数据.
- 采用功能性算力评估 - 失算电池 (FUNA-DB),这是一个在线选器,用于评估数字处理和算术流性.
- 应用确认因素分析和接收机运行特性 (ROC) 曲线分析.
主要成果:
- 确认因素分析证实了FUNA-DB的双因素结构,在人口统计和年级水平上不变.
- 数字处理技能在所有年级水平上都表现出对MLD和LA的公平分类准确性.
- 预测MLD的分类准确性高于LA的分类准确性 (切断值<5%),而不是LA (切断值<25%).
结论:
- 数字处理技能对于识别MLD和LA的学生是有价值的.
- 准确识别MLD需要评估数字处理能力和算术流性.
- 这些发现支持一种更全面的方法来诊断数学学习障碍.
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