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Shengyu Jiang; Jiaying Xiao; Chun Wang – Grantee Submission, 2022
An online learning system has the capacity to offer customized content that caters to individual learner's need and has seen growing interest from industry and academia alike in recent years. Different from traditional computerized adaptive testing setting which has a well-calibrated item bank with new items periodically added, online learning…
Descriptors: Item Response Theory, Item Banks, Bayesian Statistics, Learning Management Systems
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
Shengyu Jiang – ProQuest LLC, 2020
An online learning system has the capacity to offer customized content that caters to individual learner's need and has seen growing interest from industry and academia alike in recent years. Noting the similarity between online learning and the more established adaptive testing procedures, research has focused on applying the techniques of…
Descriptors: Item Response Theory, Item Banks, Bayesian Statistics, Learning Management Systems
Stocking, Martha L.; And Others – 1991
This paper presents a new heuristic approach to interactive test assembly that is called the successive item replacement algorithm. This approach builds on the work of W. J. van der Linden (1987) and W. J. van der Linden and E. Boekkooi-Timminga (1989) in which methods of mathematical optimization are combined with item response theory to…
Descriptors: Algorithms, Automation, Computer Selection, Heuristics

Armstrong, Ronald D.; And Others – Psychometrika, 1992
A method is presented and illustrated for simultaneously generating multiple tests with similar characteristics from the item bank by using binary programing techniques. The parallel tests are created to match an existing seed test item for item and to match user-supplied taxonomic specifications. (SLD)
Descriptors: Algorithms, Arithmetic, Computer Assisted Testing, Equations (Mathematics)
Veerkamp, Wim J. J.; Berger, Martijn P. F. – 1994
Items with the highest discrimination parameter values in a logistic item response theory (IRT) model do not necessarily give maximum information. This paper shows which discrimination parameter values (as a function of the guessing parameter and the distance between person ability and item difficulty) give maximum information for the…
Descriptors: Ability, Adaptive Testing, Algorithms, Computer Assisted Testing
Optimal Assembly of Educational and Psychological Tests, with a Bibliography. Research Report 98-05.
van der Linden, Wim J. – 1998
The advent of computers in educational and psychological measurement has lead to the need for algorithms for optimal assembly of tests from item banks. This paper reviews the literature on optimal test assembly and introduces the contributions to this report on the topic. Four different approaches to computerized test assembly are discussed:…
Descriptors: Algorithms, Computer Assisted Testing, Educational Testing, Equated Scores
Eignor, Daniel R.; And Others – 1993
The extensive computer simulation work done in developing the computer adaptive versions of the Graduate Record Examinations (GRE) Board General Test and the College Board Admissions Testing Program (ATP) Scholastic Aptitude Test (SAT) is described in this report. Both the GRE General and SAT computer adaptive tests (CATs), which are fixed length…
Descriptors: Adaptive Testing, Algorithms, Case Studies, College Entrance Examinations