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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2412.04482 |
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| _version_ | 1866916521545039872 |
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| author | Camilli, Gregory Suter, Larry |
| author_facet | Camilli, Gregory Suter, Larry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04482 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NLP Cluster Analysis of Common Core State Standards and NAEP Item Specifications Camilli, Gregory Suter, Larry Computers and Society Artificial Intelligence Computation and Language 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. |
| title | NLP Cluster Analysis of Common Core State Standards and NAEP Item Specifications |
| topic | Computers and Society Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2412.04482 |