Examinees' Rapid-Guessing Patterns in Computerized Adaptive Testing for Interim Assessment: From Hierarchical Clustering
| Autor: | Kaptur, Dandan Chen, Patton, Elizabeth, Rome, Logan |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: preprint, NCME conference paper, 37 pages (excluding references) |
| Databáze: | arXiv |
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