Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 4 |
Descriptor
Computer Assisted Testing | 12 |
Probability | 12 |
Adaptive Testing | 7 |
Bayesian Statistics | 4 |
Test Items | 4 |
Higher Education | 3 |
Item Banks | 3 |
Item Response Theory | 3 |
Simulation | 3 |
Student Evaluation | 3 |
College Students | 2 |
More ▼ |
Source
Applied Measurement in… | 1 |
Grantee Submission | 1 |
International Educational… | 1 |
International Society for… | 1 |
Mathematics Education… | 1 |
Author
Anakin, Megan | 1 |
Baeumer, Boris | 1 |
Bergstrom, Betty A. | 1 |
Coots, Madison | 1 |
DeBoer, George E. | 1 |
Frick, Theodore W. | 1 |
Goodman, Noah | 1 |
Hanif Akhtar | 1 |
Hardcastle, Joseph | 1 |
Herrmann-Abell, Cari F. | 1 |
Knowles, Andrea | 1 |
More ▼ |
Publication Type
Speeches/Meeting Papers | 12 |
Reports - Research | 7 |
Reports - Evaluative | 3 |
Journal Articles | 1 |
Opinion Papers | 1 |
Reports - Descriptive | 1 |
Education Level
Elementary Education | 1 |
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Australia | 1 |
New Zealand | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Hanif Akhtar – International Society for Technology, Education, and Science, 2023
For efficiency, Computerized Adaptive Test (CAT) algorithm selects items with the maximum information, typically with a 50% probability of being answered correctly. However, examinees may not be satisfied if they only correctly answer 50% of the items. Researchers discovered that changing the item selection algorithms to choose easier items (i.e.,…
Descriptors: Success, Probability, Computer Assisted Testing, Adaptive Testing
Malik, Ali; Wu, Mike; Vasavada, Vrinda; Song, Jinpeng; Coots, Madison; Mitchell, John; Goodman, Noah; Piech, Chris – International Educational Data Mining Society, 2021
Access to high-quality education at scale is limited by the difficulty of providing student feedback on open-ended assignments in structured domains like programming, graphics, and short response questions. This problem has proven to be exceptionally difficult: for humans, it requires large amounts of manual work, and for computers, until…
Descriptors: Grading, Accuracy, Computer Assisted Testing, Automation
Knowles, Andrea; Linsell, Chris; Baeumer, Boris; Anakin, Megan – Mathematics Education Research Group of Australasia, 2021
This paper describes the development and efficacy of an online tool for assessing the numeracy of undergraduate students. The tool was designed to be easy to administer, provide immediate feedback to students on whether they had the required level of numeracy, and to be consistent with other measures of adult numeracy. When used with students…
Descriptors: Numeracy, Probability, Undergraduate Students, Computer Assisted Testing
Hardcastle, Joseph; Herrmann-Abell, Cari F.; DeBoer, George E. – Grantee Submission, 2017
Can student performance on computer-based tests (CBT) and paper-and-pencil tests (PPT) be considered equivalent measures of student knowledge? States and school districts are grappling with this question, and although studies addressing this question are growing, additional research is needed. We report on the performance of students who took…
Descriptors: Academic Achievement, Computer Assisted Testing, Comparative Analysis, Student Evaluation
Thorndike, Robert L. – 1980
In an invitational address to the Victorian Institute of Educational Research, the author discussed Bayesian theory and its relationship to the design and construction of tailored or adaptive tests. Bayesian thinking involves recognizing the role of prior probabilities and using these probabilities in combination with new data to arrive at future…
Descriptors: Adaptive Testing, Bayesian Statistics, Computer Assisted Testing, Error of Measurement
Patsula, Liane N.; Steffen, Mandred – 1997
One challenge associated with computerized adaptive testing (CAT) is the maintenance of test and item security while allowing for daily testing. An alternative to continually creating new pools containing an independent set of items would be to consider each CAT pool as a sample of items from a larger collection (referred to as a VAT) rather than…
Descriptors: Adaptive Testing, Computer Assisted Testing, Item Banks, Multiple Choice Tests
Zwick, Rebecca – 1995
This paper describes a study, now in progress, of new methods for representing the sampling variability of Mantel-Haenszel differential item functioning (DIF) results, based on the system for categorizing the severity of DIF that is now in place at the Educational Testing Service. The methods, which involve a Bayesian elaboration of procedures…
Descriptors: Adaptive Testing, Bayesian Statistics, Classification, Computer Assisted Testing
Frick, Theodore W.; And Others – 1989
Expert systems can be used to aid decision making. A computerized adaptive test (CAT) is one kind of expert system, although it is not commonly recognized as such. A new approach, termed EXSPRT, was devised that combines expert systems reasoning and sequential probability ratio test stopping rules. EXSPRT-R uses random selection of test items,…
Descriptors: Adaptive Testing, College Students, Computer Assisted Testing, Expert Systems
Lazarte, Alejandro A. – 1999
Two experiments reproduced in a simulated computerized test-taking situation the effect of two of the main determinants in answering an item in a test: the difficulty of the item and the time available to answer it. A model is proposed for the time to respond or abandon an item and for the probability of abandoning it or answering it correctly. In…
Descriptors: Computer Assisted Testing, Difficulty Level, Higher Education, Probability
Reckase, Mark D. – 1979
This paper describes two procedures for making binary classification decisions using tailored testing: the sequential probability ratio test (SPRT) and a Bayesian decision procedure. The first procedure described, the SPRT, was developed by Wald for quality control work. It has not been widely applied for testing applications because the…
Descriptors: Adaptive Testing, Bayesian Statistics, Computer Assisted Testing, Criterion Referenced Tests

Bergstrom, Betty A.; And Others – Applied Measurement in Education, 1992
Effects of altering test difficulty on examinee ability measures and test length in a computer adaptive test were studied for 225 medical technology students in 3 test difficulty conditions. Results suggest that, with an item pool of sufficient depth and breadth, acceptable targeting to test difficulty is possible. (SLD)
Descriptors: Ability, Adaptive Testing, Change, College Students
Tatsuoka, Kikumi K.; Tatsuoka, Maurice M. – 1986
The rule space model permits measurement of cognitive skill acquisition, diagnosis of cognitive errors, and detection of the strengths and weaknesses of knowledge possessed by individuals. Two ways to classify an individual into his or her most plausible latent state of knowledge include: (1) hypothesis testing--Bayes' decision rules for minimum…
Descriptors: Artificial Intelligence, Bayesian Statistics, Cognitive Development, Computer Assisted Testing