围绕多种知识结构和多种功能进行区分
1School of Mathematical Sciences, Soochow University, Suzhou, China.
The British journal of mathematical and statistical psychology
|November 18, 2024
概括
本研究概括了多元知识空间理论 (KST) 中的可区分性. 它建立了不同多种类型的KST组件之间的等价值,从而实现了更广泛的应用.
科学领域:
- 认知科学 认知科学
- 数学心理学 数学心理学
- 教育测量教育的测量
背景情况:
- 区分能力的概念最近在多种知识空间理论 (KST) 中引入.
- 多种类型的KST将传统的KST扩展到具有多个响应选择或掌握水平的情况.
- 了解区分性对于评估知识空间的结构和属性至关重要.
研究的目的:
- 探索和定义在细粒度的多元知识空间的背景下的可区分性.
- 调查多元猜测函数,技能函数,知识结构和知识空间中的可区分性之间的关系.
- 提供从KST到其多种扩展的可歧视性的系统概括.
主要方法:
- 在多种知识空间模型中对可区分性的理论分析.
- 建立多种类型的KST的不同组件之间的数学等价值.
- 在各种多种类型的KST结构中对可歧视性措施的比较分析.
主要成果:
- 在多元猜测函数和细粒度多元知识空间的可区分性之间证明了等价性.
- 在多种技能功能和多种知识结构的可区分性之间也显示了等同性.
- 这些发现证实了这些多种类型的KST元素在可歧视性方面的相互联系.
结论:
- 这项研究成功地将可区分性概念概括为多元知识空间理论.
- 已建立的等值为在KST中更统一,更系统地对待歧视性提供了基础.
- 这项工作为在复杂的评估场景中对可歧视性进行高级分析和应用开辟了道路.
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