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Kylie Gorney; Mark D. Reckase – Journal of Educational Measurement, 2025
In computerized adaptive testing, item exposure control methods are often used to provide a more balanced usage of the item pool. Many of the most popular methods, including the restricted method (Revuelta and Ponsoda), use a single maximum exposure rate to limit the proportion of times that each item is administered. However, Barrada et al.…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Item Banks
Wallace N. Pinto Jr.; Jinnie Shin – Journal of Educational Measurement, 2025
In recent years, the application of explainability techniques to automated essay scoring and automated short-answer grading (ASAG) models, particularly those based on transformer architectures, has gained significant attention. However, the reliability and consistency of these techniques remain underexplored. This study systematically investigates…
Descriptors: Automation, Grading, Computer Assisted Testing, Scoring
Tahereh Firoozi; Hamid Mohammadi; Mark J. Gierl – Journal of Educational Measurement, 2025
The purpose of this study is to describe and evaluate a multilingual automated essay scoring (AES) system for grading essays in three languages. Two different sentence embedding models were evaluated within the AES system, multilingual BERT (mBERT) and language-agnostic BERT sentence embedding (LaBSE). German, Italian, and Czech essays were…
Descriptors: College Students, Slavic Languages, German, Italian
Bengs, Daniel; Kroehne, Ulf; Brefeld, Ulf – Journal of Educational Measurement, 2021
By tailoring test forms to the test-taker's proficiency, Computerized Adaptive Testing (CAT) enables substantial increases in testing efficiency over fixed forms testing. When used for formative assessment, the alignment of task difficulty with proficiency increases the chance that teachers can derive useful feedback from assessment data. The…
Descriptors: Computer Assisted Testing, Formative Evaluation, Group Testing, Program Effectiveness
Dorsey, David W.; Michaels, Hillary R. – Journal of Educational Measurement, 2022
We have dramatically advanced our ability to create rich, complex, and effective assessments across a range of uses through technology advancement. Artificial Intelligence (AI) enabled assessments represent one such area of advancement--one that has captured our collective interest and imagination. Scientists and practitioners within the domains…
Descriptors: Validity, Ethics, Artificial Intelligence, Evaluation Methods
van der Linden, Wim J.; Breithaupt, Krista; Chuah, Siang Chee; Zhang, Yanwei – Journal of Educational Measurement, 2007
A potential undesirable effect of multistage testing is differential speededness, which happens if some of the test takers run out of time because they receive subtests with items that are more time intensive than others. This article shows how a probabilistic response-time model can be used for estimating differences in time intensities and speed…
Descriptors: Adaptive Testing, Evaluation Methods, Test Items, Reaction Time

Eignor, Daniel R. – Journal of Educational Measurement, 1997
The authors of the "Guidelines," a task force of eight, intend to present an organized list of features to be considered in reporting or evaluating computerized-adaptive assessments. Apart from a few weaknesses, the book is a useful and complete document that will be very helpful to test developers. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Evaluation Methods, Guidelines
Lei, Pui-Wa; Chen, Shu-Ying; Yu, Lan – Journal of Educational Measurement, 2006
Mantel-Haenszel and SIBTEST, which have known difficulty in detecting non-unidirectional differential item functioning (DIF), have been adapted with some success for computerized adaptive testing (CAT). This study adapts logistic regression (LR) and the item-response-theory-likelihood-ratio test (IRT-LRT), capable of detecting both unidirectional…
Descriptors: Evaluation Methods, Test Bias, Computer Assisted Testing, Multiple Regression Analysis