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Carol Eckerly; Yue Jia; Paul Jewsbury – ETS Research Report Series, 2022
Testing programs have explored the use of technology-enhanced items alongside traditional item types (e.g., multiple-choice and constructed-response items) as measurement evidence of latent constructs modeled with item response theory (IRT). In this report, we discuss considerations in applying IRT models to a particular type of adaptive testlet…
Descriptors: Computer Assisted Testing, Test Items, Item Response Theory, Scoring
Wang, Wenhao; Kingston, Neal M.; Davis, Marcia H.; Tiemann, Gail C.; Tonks, Stephen; Hock, Michael – Educational Measurement: Issues and Practice, 2021
Adaptive tests are more efficient than fixed-length tests through the use of item response theory; adaptive tests also present students questions that are tailored to their proficiency level. Although the adaptive algorithm is straightforward, developing a multidimensional computer adaptive test (MCAT) measure is complex. Evidence-centered design…
Descriptors: Evidence Based Practice, Reading Motivation, Adaptive Testing, Computer Assisted Testing
Nixi Wang – ProQuest LLC, 2022
Measurement errors attributable to cultural issues are complex and challenging for educational assessments. We need assessment tests sensitive to the cultural heterogeneity of populations, and psychometric methods appropriate to address fairness and equity concerns. Built on the research of culturally responsive assessment, this dissertation…
Descriptors: Culturally Relevant Education, Testing, Equal Education, Validity
Aybek, Eren Can; Demirtasli, R. Nukhet – International Journal of Research in Education and Science, 2017
This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Test Items
Albacete, Patricia; Silliman, Scott; Jordan, Pamela – Grantee Submission, 2017
Intelligent tutoring systems (ITS), like human tutors, try to adapt to student's knowledge level so that the instruction is tailored to their needs. One aspect of this adaptation relies on the ability to have an understanding of the student's initial knowledge so as to build on it, avoiding teaching what the student already knows and focusing on…
Descriptors: Intelligent Tutoring Systems, Knowledge Level, Multiple Choice Tests, Computer Assisted Testing
Hsu, Chia-Ling; Wang, Wen-Chung – Journal of Educational Measurement, 2015
Cognitive diagnosis models provide profile information about a set of latent binary attributes, whereas item response models yield a summary report on a latent continuous trait. To utilize the advantages of both models, higher order cognitive diagnosis models were developed in which information about both latent binary attributes and latent…
Descriptors: Computer Assisted Testing, Adaptive Testing, Models, Cognitive Measurement
Nydick, Steven W. – Journal of Educational and Behavioral Statistics, 2014
The sequential probability ratio test (SPRT) is a common method for terminating item response theory (IRT)-based adaptive classification tests. To decide whether a classification test should stop, the SPRT compares a simple log-likelihood ratio, based on the classification bound separating two categories, to prespecified critical values. As has…
Descriptors: Probability, Item Response Theory, Models, Classification
Han, Kyung T.; Rudner, Lawrence M. – Graduate Management Admission Council, 2014
This study uses mixed integer quadratic programming (MIQP) to construct multiple highly equivalent item pools simultaneously, and compares the results from mixed integer programming (MIP). Three different MIP/MIQP models were implemented and evaluated using real CAT item pool data with 23 different content areas and a goal of equal information…
Descriptors: Item Banks, Programming, Computer Assisted Testing, Adaptive Testing
Seo, Dong Gi; Weiss, David J. – Educational and Psychological Measurement, 2015
Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm…
Descriptors: Computer Assisted Testing, Adaptive Testing, Accuracy, Fidelity
Hsu, Chia-Ling; Wang, Wen-Chung; Chen, Shu-Ying – Applied Psychological Measurement, 2013
Interest in developing computerized adaptive testing (CAT) under cognitive diagnosis models (CDMs) has increased recently. CAT algorithms that use a fixed-length termination rule frequently lead to different degrees of measurement precision for different examinees. Fixed precision, in which the examinees receive the same degree of measurement…
Descriptors: Computer Assisted Testing, Adaptive Testing, Cognitive Tests, Diagnostic Tests
Choi, Seung W.; Podrabsky, Tracy; McKinney, Natalie – Applied Psychological Measurement, 2012
Computerized adaptive testing (CAT) enables efficient and flexible measurement of latent constructs. The majority of educational and cognitive measurement constructs are based on dichotomous item response theory (IRT) models. An integral part of developing various components of a CAT system is conducting simulations using both known and empirical…
Descriptors: Computer Assisted Testing, Adaptive Testing, Computer Software, Item Response Theory
Wang, Wen-Chung; Liu, Chen-Wei; Wu, Shiu-Lien – Applied Psychological Measurement, 2013
The random-threshold generalized unfolding model (RTGUM) was developed by treating the thresholds in the generalized unfolding model as random effects rather than fixed effects to account for the subjective nature of the selection of categories in Likert items. The parameters of the new model can be estimated with the JAGS (Just Another Gibbs…
Descriptors: Computer Assisted Testing, Adaptive Testing, Models, Bayesian Statistics
Jiao, Hong; Macready, George; Liu, Junhui; Cho, Youngmi – Applied Psychological Measurement, 2012
This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…
Descriptors: Item Banks, Adaptive Testing, Computer Assisted Testing, Identification
Thompson, Nathan A.; Weiss, David J. – Practical Assessment, Research & Evaluation, 2011
A substantial amount of research has been conducted over the past 40 years on technical aspects of computerized adaptive testing (CAT), such as item selection algorithms, item exposure controls, and termination criteria. However, there is little literature providing practical guidance on the development of a CAT. This paper seeks to collate some…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Construction, Models
Yen, Yung-Chin; Ho, Rong-Guey; Liao, Wen-Wei; Chen, Li-Ju – Educational Technology & Society, 2012
In a test, the testing score would be closer to examinee's actual ability when careless mistakes were corrected. In CAT, however, changing the answer of one item in CAT might cause the following items no longer appropriate for estimating the examinee's ability. These inappropriate items in a reviewable CAT might in turn introduce bias in ability…
Descriptors: Foreign Countries, Adaptive Testing, Computer Assisted Testing, Item Response Theory