Examinees' Rapid-Guessing Patterns in Computerized Adaptive Testing for Interim Assessment: From Hierarchical Clustering
Abstract
Interim assessment is frequently administered via computerized adaptive testing (CAT), offering direct support to teaching and learning. This study attempted to fill a vital knowledge gap about the nuanced landscape of examinees' rapid-guessing patterns in CAT in the interim assessment context. We analyzed a sample of 146,519 examinees in Grades 1-8 who participated in a widely used CAT, using hierarchical clustering, a robust data science methodology for uncovering insights in data. We found that examinees' rapid-guessing patterns varied across item positions, content domains, chronological grades, examinee clusters, and examinees' overall rapid-guessing level on the test, suggesting a nuanced interplay between testing features and examinees' behavior. Our study contributes to the literature on rapid guessing in CATs for interim assessment, offering a comprehensive and nuanced pattern analysis and demonstrating the application of hierarchical clustering to process data analysis in testing.
Cite
@article{arxiv.2408.11716,
title = {Examinees' Rapid-Guessing Patterns in Computerized Adaptive Testing for Interim Assessment: From Hierarchical Clustering},
author = {Dandan Chen Kaptur and Elizabeth Patton and Logan Rome},
journal= {arXiv preprint arXiv:2408.11716},
year = {2024}
}
Comments
preprint, NCME conference paper, 37 pages (excluding references)