用NLP分析美国教育标准与试题规范的语义差异，验证分类体系有效性。
NLP Cluster Analysis of Common Core State Standards and NAEP Item Specifications
- 对核心标准与NAEP试题规范的嵌入向量分别进行k-means聚类
- 发现两类分类体系在语义上具有显著区分性，支持其独立性
- 为教育评估标准映射提供可量化的技术依据，适合教育政策研究者
Camilli (2024) 提出一种利用自然语言处理（NLP）映射内容标准与试题规范关系的方法。本研究通过分析标准与试题规范的名义分类，检验其构念等价性。本文进一步考察这些分类——即核心标准的'领域'（domains）与国家教育进展评估（NAEP）试题规范的'纲领'（strands）——在语义上的实证区分度。方法上，对标准与规范的嵌入向量分别执行k-means聚类。结果表明，两类分类在语义空间中具有显著区分性，支持其作为独立语义单元的有效性。最后简要展示该发现的应用潜力。
原文摘要 · Abstract (English)
Camilli (2024) proposed a methodology using natural language processing (NLP) to map the relationship of a set of content standards to item specifications. This study provided evidence that NLP can be used to improve the mapping process. As part of this investigation, the nominal classifications of standards and items specifications were used to examine construct equivalence. In the current paper, we determine the strength of empirical support for the semantic distinctiveness of these classifications, which are known as "domains" for Common Core standards, and "strands" for National Assessment of Educational Progress (NAEP) item specifications. This is accomplished by separate k-means clustering for standards and specifications of their corresponding embedding vectors. We then briefly illustrate an application of these findings.
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