超越线性:使用IRT缩放的水平模型来描述先前的比例推理技能和分数学习结果之间的关系
Constanze Schadl1,2, Stefan Ufer3
1Department of Education, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany.
Child development
|July 24, 2023
概括
在比例推理中掌握自然和内部理性比率是成功学习分数的关键. 这项研究探讨了比例推理技能如何影响简单线性之外的分数结果.
科学领域:
- 教育心理学教育心理学
- 数学教育教育 数学教育
背景情况:
- 之前的研究表明,先前的数学技能与以后的数学学习之间存在正相关性.
- 了解先前的技能水平和学习成果之间的具体关系对于推进数学概念开发至关重要.
研究的目的:
- 探索象征性比例推理技能和分数学习结果之间的关系.
- 通过使用水平模型,超越线性模型来研究这种关系.
主要方法:
- 利用了2017年规模化研究 (N=325) 和2018/2019德国四年级到六年级学生的纵向研究 (N=436) 的数据.
- 用人级模型来分析比例推理和分数结果之间的关联.
主要成果:
- 在比例推理中掌握自然和内部理性比率似乎对有效的分数学习尤为重要.
- 这些发现表明,这种细微的关系超越了简单的"更多是更好的"范式.
结论:
- 比例推理的特定方面,即自然和内部理性比率,对于成功获得分数至关重要.
- 这项研究通过详细说明先前技能如何影响学习成果,从而促进了对数学概念开发的理解.
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