Abstract
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.