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Harold Doran; Testsuhiro Yamada; Ted Diaz; Emre Gonulates; Vanessa Culver – Journal of Educational Measurement, 2025
Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
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Ersen, Rabia Karatoprak; Lee, Won-Chan – Journal of Educational Measurement, 2023
The purpose of this study was to compare calibration and linking methods for placing pretest item parameter estimates on the item pool scale in a 1-3 computerized multistage adaptive testing design in terms of item parameter recovery. Two models were used: embedded-section, in which pretest items were administered within a separate module, and…
Descriptors: Pretesting, Test Items, Computer Assisted Testing, Adaptive Testing
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Yuan, Lu; Huang, Yingshi; Li, Shuhang; Chen, Ping – Journal of Educational Measurement, 2023
Online calibration is a key technology for item calibration in computerized adaptive testing (CAT) and has been widely used in various forms of CAT, including unidimensional CAT, multidimensional CAT (MCAT), CAT with polytomously scored items, and cognitive diagnostic CAT. However, as multidimensional and polytomous assessment data become more…
Descriptors: Computer Assisted Testing, Adaptive Testing, Computation, Test Items
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Tan, Qingrong; Cai, Yan; Luo, Fen; Tu, Dongbo – Journal of Educational and Behavioral Statistics, 2023
To improve the calibration accuracy and calibration efficiency of cognitive diagnostic computerized adaptive testing (CD-CAT) for new items and, ultimately, contribute to the widespread application of CD-CAT in practice, the current article proposed a Gini-based online calibration method that can simultaneously calibrate the Q-matrix and item…
Descriptors: Cognitive Tests, Computer Assisted Testing, Adaptive Testing, Accuracy
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TsungHan Ho – Applied Measurement in Education, 2023
An operational multistage adaptive test (MST) requires the development of a large item bank and the effort to continuously replenish the item bank due to concerns about test security and validity over the long term. New items should be pretested and linked to the item bank before being used operationally. The linking item volume fluctuations in…
Descriptors: Bayesian Statistics, Regression (Statistics), Test Items, Pretesting
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Chen, Chia-Wen; Wang, Wen-Chung; Chiu, Ming Ming; Ro, Sage – Journal of Educational Measurement, 2020
The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
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Jewsbury, Paul A.; van Rijn, Peter W. – Journal of Educational and Behavioral Statistics, 2020
In large-scale educational assessment data consistent with a simple-structure multidimensional item response theory (MIRT) model, where every item measures only one latent variable, separate unidimensional item response theory (UIRT) models for each latent variable are often calibrated for practical reasons. While this approach can be valid for…
Descriptors: Item Response Theory, Computation, Test Items, Adaptive Testing
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Chun Wang; Ping Chen; Shengyu Jiang – Journal of Educational Measurement, 2020
Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait [theta] estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence, questions remain as to how to…
Descriptors: Test Construction, Test Items, Adaptive Testing, Maximum Likelihood Statistics
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Öztürk, Nagihan Boztunç – Universal Journal of Educational Research, 2019
In this study, how the length and characteristics of routing module in different panel designs affect measurement precision is examined. In the scope of the study, six different routing module length, nine different routing module characteristics, and two different panel design are handled. At the end of the study, the effects of conditions on…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Length, Test Format
Wang, Chun; Chen, Ping; Jiang, Shengyu – Grantee Submission, 2019
Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait [theta] estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence questions remain as to how to…
Descriptors: Adaptive Testing, Test Items, Item Response Theory, Maximum Likelihood Statistics
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Gawliczek, Piotr; Krykun, Viktoriia; Tarasenko, Nataliya; Tyshchenko, Maksym; Shapran, Oleksandr – Advanced Education, 2021
The article deals with the innovative, cutting age solution within the language testing realm, namely computer adaptive language testing (CALT) in accordance with the NATO Standardization Agreement 6001 (NATO STANAG 6001) requirements for further implementation in foreign language training of personnel of the Armed Forces of Ukraine (AF of…
Descriptors: Computer Assisted Testing, Adaptive Testing, Language Tests, Second Language Instruction
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Sahin, Alper; Ozbasi, Durmus – Eurasian Journal of Educational Research, 2017
Purpose: This study aims to reveal effects of content balancing and item selection method on ability estimation in computerized adaptive tests by comparing Fisher's maximum information (FMI) and likelihood weighted information (LWI) methods. Research Methods: Four groups of examinees (250, 500, 750, 1000) and a bank of 500 items with 10 different…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Test Content
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Lin, Yin; Brown, Anna – Educational and Psychological Measurement, 2017
A fundamental assumption in computerized adaptive testing is that item parameters are invariant with respect to context--items surrounding the administered item. This assumption, however, may not hold in forced-choice (FC) assessments, where explicit comparisons are made between items included in the same block. We empirically examined the…
Descriptors: Personality Measures, Measurement Techniques, Context Effect, Test Items
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Moothedath, Shana; Chaporkar, Prasanna; Belur, Madhu N. – Perspectives in Education, 2016
In recent years, the computerised adaptive test (CAT) has gained popularity over conventional exams in evaluating student capabilities with desired accuracy. However, the key limitation of CAT is that it requires a large pool of pre-calibrated questions. In the absence of such a pre-calibrated question bank, offline exams with uncalibrated…
Descriptors: Guessing (Tests), Computer Assisted Testing, Adaptive Testing, Maximum Likelihood Statistics
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Thissen, David – Journal of Educational and Behavioral Statistics, 2016
David Thissen, a professor in the Department of Psychology and Neuroscience, Quantitative Program at the University of North Carolina, has consulted and served on technical advisory committees for assessment programs that use item response theory (IRT) over the past couple decades. He has come to the conclusion that there are usually two purposes…
Descriptors: Item Response Theory, Test Construction, Testing Problems, Student Evaluation
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