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Showing 1 to 15 of 22 results Save | Export
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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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Pan, Yiqin; Livne, Oren; Wollack, James A.; Sinharay, Sandip – Educational Measurement: Issues and Practice, 2023
In computerized adaptive testing, overexposure of items in the bank is a serious problem and might result in item compromise. We develop an item selection algorithm that utilizes the entire bank well and reduces the overexposure of items. The algorithm is based on collaborative filtering and selects an item in two stages. In the first stage, a set…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
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Jyoti Prakash Meher; Rajib Mall – IEEE Transactions on Education, 2025
Contribution: This article suggests a novel method for diagnosing a learner's cognitive proficiency using deep neural networks (DNNs) based on her answers to a series of questions. The outcome of the forecast can be used for adaptive assistance. Background: Often a learner spends considerable amounts of time in attempting questions on the concepts…
Descriptors: Cognitive Ability, Assistive Technology, Adaptive Testing, Computer Assisted Testing
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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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Michael Bass; Scott Morris; Sheng Zhang – Measurement: Interdisciplinary Research and Perspectives, 2025
Administration of patient-reported outcome measures (PROs), using multidimensional computer adaptive tests (MCATs) has the potential to reduce patient burden, but the efficiency of MCAT depends on the degree to which an individual's responses fit the psychometric properties of the assessment. Assessing patients' symptom burden through the…
Descriptors: Adaptive Testing, Computer Assisted Testing, Patients, Outcome Measures
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Gorgun, Guher; Bulut, Okan – Large-scale Assessments in Education, 2023
In low-stakes assessment settings, students' performance is not only influenced by students' ability level but also their test-taking engagement. In computerized adaptive tests (CATs), disengaged responses (e.g., rapid guesses) that fail to reflect students' true ability levels may lead to the selection of less informative items and thereby…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
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Liou, Gloria; Bonner, Cavan V.; Tay, Louis – International Journal of Testing, 2022
With the advent of big data and advances in technology, psychological assessments have become increasingly sophisticated and complex. Nevertheless, traditional psychometric issues concerning the validity, reliability, and measurement bias of such assessments remain fundamental in determining whether score inferences of human attributes are…
Descriptors: Psychometrics, Computer Assisted Testing, Adaptive Testing, Data
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Wim J. van der Linden; Luping Niu; Seung W. Choi – Journal of Educational and Behavioral Statistics, 2024
A test battery with two different levels of adaptation is presented: a within-subtest level for the selection of the items in the subtests and a between-subtest level to move from one subtest to the next. The battery runs on a two-level model consisting of a regular response model for each of the subtests extended with a second level for the joint…
Descriptors: Adaptive Testing, Test Construction, Test Format, Test Reliability
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Cheng, Philip E.; Liou, Michelle – Applied Psychological Measurement, 2000
Reviewed methods of estimating theta suitable for computerized adaptive testing (CAT) and discussed the differences between Fisher and Kullback-Leibler information criteria for selecting items. Examined the accuracy of different CAT algorithms using samples from the National Assessment of Educational Progress. Results show when correcting for…
Descriptors: Ability, Adaptive Testing, Algorithms, Computer Assisted Testing
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Stocking, Martha L.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 1998
Ensuring item and pool security in a continuous testing environment is explored through a new method of controlling exposure rate of items conditional on ability level in computerized testing. Properties of this conditional control on exposure rate, when used in conjunction with a particular adaptive testing algorithm, are explored using simulated…
Descriptors: Adaptive Testing, Algorithms, Computer Assisted Testing, Difficulty Level
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van der Linden, Wim J. – Applied Psychological Measurement, 2001
Presents a constrained computerized adaptive testing (CAT) algorithm that can be used to equate CAT number-correct scores to a reference test. Used an item bank from the Law School Admission Test to compare results of the algorithm with those for equipercentile observed-score equating. Discusses advantages of the approach. (SLD)
Descriptors: Adaptive Testing, Algorithms, Computer Assisted Testing, Equated Scores
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Eggen, T. J. H. M. – Applied Psychological Measurement, 1999
Evaluates a method for item selection in adaptive testing that is based on Kullback-Leibler information (KLI) (T. Cover and J. Thomas, 1991). Simulation study results show that testing algorithms using KLI-based item selection perform better than or as well as those using Fisher information item selection. (SLD)
Descriptors: Adaptive Testing, Algorithms, Computer Assisted Testing, Selection
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Schnipke, Deborah L.; Green, Bert F. – Journal of Educational Measurement, 1995
Two item selection algorithms, one based on maximal differentiation between examinees and one based on item response theory and maximum information for each examinee, were compared in simulated linear and adaptive tests of cognitive ability. Adaptive tests based on maximum information were clearly superior. (SLD)
Descriptors: Adaptive Testing, Algorithms, Comparative Analysis, Item Response Theory
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van der Linden, Wim J. – Journal of Educational and Behavioral Statistics, 1999
Proposes an algorithm that minimizes the asymptotic variance of the maximum-likelihood (ML) estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to evaluate. Also shows how the algorithm can be modified if the interest is in a test with a "simple ability structure."…
Descriptors: Ability, Adaptive Testing, Algorithms, Computer Assisted Testing
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O'Neill, Thomas; Lunz, Mary E.; Thiede, Keith – Journal of Applied Measurement, 2000
Studied item exposure in a computerized adaptive test when the item selection algorithm presents examinees with questions they were asked in a previous test administration. Results with 178 repeat examinees on a medical technologists' test indicate that the combined use of an adaptive algorithm to select items and latent trait theory to estimate…
Descriptors: Adaptive Testing, Algorithms, Computer Assisted Testing, Item Response Theory
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