Computerized Adaptive Testing Using a Class of High-Order Item Response Theory Models

Hung Yu Huang, Po Hsi Chen, Wen Chung Wang

研究成果: 雜誌貢獻期刊論文同行評審

10 引文 斯高帕斯(Scopus)

摘要

In the human sciences, a common assumption is that latent traits have a hierarchical structure. Higher order item response theory models have been developed to account for this hierarchy. In this study, computerized adaptive testing (CAT) algorithms based on these kinds of models were implemented, and their performance under a variety of situations was examined using simulations. The results showed that the CAT algorithms were very effective. The progressive method for item selection, the Sympson and Hetter method with online and freeze procedure for item exposure control, and the multinomial model for content balancing can simultaneously maintain good measurement precision, item exposure control, content balance, test security, and pool usage.

原文英語
頁(從 - 到)689-706
頁數18
期刊Applied Psychological Measurement
36
發行號8
DOIs
出版狀態已發佈 - 2012 十一月

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)

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