Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 4 |
Descriptor
| Ability | 7 |
| Simulation | 6 |
| Computer Assisted Testing | 4 |
| Test Items | 4 |
| Adaptive Testing | 3 |
| Classification | 2 |
| Estimation (Mathematics) | 2 |
| Item Response Theory | 2 |
| Maximum Likelihood Statistics | 2 |
| Models | 2 |
| Probability | 2 |
| More ▼ | |
Source
| Journal of Educational and… | 2 |
| Applied Psychological… | 1 |
| Childhood Education | 1 |
| Educational and Psychological… | 1 |
| Journal of Educational… | 1 |
Author
Publication Type
| Reports - Descriptive | 7 |
| Journal Articles | 6 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
| Law School Admission Test | 1 |
What Works Clearinghouse Rating
Ranger, Jochen; Kuhn, Jörg-Tobias; Wolgast, Anett – Journal of Educational Measurement, 2021
Van der Linden's hierarchical model for responses and response times can be used in order to infer the ability and mental speed of test takers from their responses and response times in an educational test. A standard approach for this is maximum likelihood estimation. In real-world applications, the data of some test takers might be partly…
Descriptors: Models, Reaction Time, Item Response Theory, Tests
Yadav, Savita; Chakraborty, Pinaki – Childhood Education, 2021
Children like to play with smartphones and other touchscreen-based devices, and suitably developed apps can help in entertaining, nurturing, and educating children. For the last three years, the authors have been studying how children interact with smartphones and other touchscreen-based devices. They assert that knowing the capabilities of…
Descriptors: Educational Technology, Technology Uses in Education, Handheld Devices, Telecommunications
Schuster, Christof; Yuan, Ke-Hai – Journal of Educational and Behavioral Statistics, 2011
Because of response disturbances such as guessing, cheating, or carelessness, item response models often can only approximate the "true" individual response probabilities. As a consequence, maximum-likelihood estimates of ability will be biased. Typically, the nature and extent to which response disturbances are present is unknown, and, therefore,…
Descriptors: Computation, Item Response Theory, Probability, Maximum Likelihood Statistics
Wang, Wen-Chung; Huang, Sheng-Yun – Educational and Psychological Measurement, 2011
The one-parameter logistic model with ability-based guessing (1PL-AG) has been recently developed to account for effect of ability on guessing behavior in multiple-choice items. In this study, the authors developed algorithms for computerized classification testing under the 1PL-AG and conducted a series of simulations to evaluate their…
Descriptors: Computer Assisted Testing, Classification, Item Analysis, Probability
Peer reviewedVeerkamp, Wim J. J. – Journal of Educational and Behavioral Statistics, 2000
Showed how Taylor approximation can be used to generate a linear approximation to a logistic item characteristic curve and a linear ability estimator. Demonstrated how, for a specific simulation, this could result in the special case of a Robbins-Monro item selection procedure for adaptive testing. (SLD)
Descriptors: Ability, Adaptive Testing, Computer Assisted Testing, Selection
Peer reviewedvan der Linden, Wim J.; Reese, Lynda M. – Applied Psychological Measurement, 1998
Proposes a model for constrained computerized adaptive testing in which the information in the test at the trait level (theta) estimate is maximized subject to the number of possible constraints on the content of the test. Test assembly relies on a linear-programming approach. Illustrates the approach through simulation with items from the Law…
Descriptors: Ability, Adaptive Testing, Computer Assisted Testing, Estimation (Mathematics)
Veldkamp, Bernard P.; van der Linden, Wim J. – 1999
A method of item pool design is proposed that uses an optimal blueprint for the item pool calculated from the test specifications. The blueprint is a document that specifies the attributes that the items in the computerized adaptive test (CAT) pool should have. The blueprint can be a starting point for the item writing process, and it can be used…
Descriptors: Ability, Adaptive Testing, Classification, Computer Assisted Testing

Direct link
