NLP Cluster Analysis of Common Core State Standards and NAEP Item Specifications
This work addresses the challenge of aligning educational standards with assessments for educators and policymakers, but it is incremental as it builds on prior methodology.
The study tackled the problem of mapping educational standards to assessment items by using NLP and k-means clustering to evaluate the semantic distinctiveness of classifications like domains and strands, finding that NLP can improve the mapping process.
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.