An NLP Crosswalk Between the Common Core State Standards and NAEP Item Specifications

TL;DR

This paper presents an NLP-based method using embedding vectors and multivariate similarity to align educational content standards with assessment item specifications, demonstrated on CCSS and NAEP data.

Contribution

It introduces a hybrid regression approach leveraging embeddings to automate the crosswalk between standards and assessment items.

Findings

Effective matching of CCSS to NAEP item specifications.

Demonstrated the use of multivariate similarity for content alignment.

Supports scalable, automated content standard mapping.

Abstract

Natural language processing (NLP) is rapidly developing for applications in educational assessment. In this paper, I describe an NLP-based procedure that can be used to support subject matter experts in establishing a crosswalk between item specifications and content standards. This paper extends recent work by proposing and demonstrating the use of multivariate similarity based on embedding vectors for sentences or texts. In particular, a hybrid regression procedure is demonstrated for establishing the match of each content standard to multiple item specifications. The procedure is used to evaluate the match of the Common Core State Standards (CCSS) for mathematics at grade 4 to the corresponding item specifications for the 2026 National Assessment of Educational Progress (NAEP).