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Xin Qiao; Akihito Kamata; Yusuf Kara; Cornelis Potgieter; Joseph Nese – Grantee Submission, 2023
In this article, the beta-binomial model for count data is proposed and demonstrated in terms of its application in the context of oral reading fluency (ORF) assessment, where the number of words read correctly (WRC) is of interest. Existing studies adopted the binomial model for count data in similar assessment scenarios. The beta-binomial model,…
Descriptors: Oral Reading, Reading Fluency, Bayesian Statistics, Markov Processes
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
Lau, C. Allen; Wang, Tianyou – 1999
A study was conducted to extend the sequential probability ratio testing (SPRT) procedure with the polytomous model under some practical constraints in computerized classification testing (CCT), such as methods to control item exposure rate, and to study the effects of other variables, including item information algorithms, test difficulties, item…
Descriptors: Algorithms, Computer Assisted Testing, Difficulty Level, Item Banks
Lau, C. Allen; Wang, Tianyou – 1998
The purposes of this study were to: (1) extend the sequential probability ratio testing (SPRT) procedure to polytomous item response theory (IRT) models in computerized classification testing (CCT); (2) compare polytomous items with dichotomous items using the SPRT procedure for their accuracy and efficiency; (3) study a direct approach in…
Descriptors: Computer Assisted Testing, Cutting Scores, Item Response Theory, Mastery Tests
Lau, Che-Ming Allen; And Others – 1996
This study focused on the robustness of unidimensional item response theory (UIRT) models in computerized classification testing against violation of the unidimensionality assumption. The study addressed whether UIRT models remain acceptable under various testing conditions and dimensionality strengths. Monte Carlo simulation techniques were used…
Descriptors: Classification, Computer Assisted Testing, Educational Testing, Item Response Theory
Parshall, Cynthia G.; Kromrey, Jeffrey D.; Harmes, J. Christine; Sentovich, Christina – 2001
Computerized adaptive tests (CATs) are efficient because of their optimal item selection procedures that target maximally informative items at each estimated ability level. However, operational administration of these optimal CATs results in a relatively small subset of items given to examinees too often, while another portion of the item pool is…
Descriptors: Ability, Adaptive Testing, Computer Assisted Testing, Estimation (Mathematics)
Lau, C. Allen; Wang, Tianyou – 2000
This paper proposes a new Information-Time index as the basis for item selection in computerized classification testing (CCT) and investigates how this new item selection algorithm can help improve test efficiency for item pools with mixed item types. It also investigates how practical constraints such as item exposure rate control, test…
Descriptors: Algorithms, Classification, Computer Assisted Testing, Elementary Secondary Education
Chang, Shun-Wen; Twu, Bor-Yaun – 1998
This study investigated and compared the properties of five methods of item exposure control within the purview of estimating examinees' abilities in a computerized adaptive testing (CAT) context. Each of the exposure control algorithms was incorporated into the item selection procedure and the adaptive testing progressed based on the CAT design…
Descriptors: Adaptive Testing, Algorithms, Comparative Analysis, Computer Assisted Testing
Weiss, David J.; Suhadolnik, Debra – 1982
The present monte carlo simulation study was designed to examine the effects of multidimensionality during the administration of computerized adaptive testing (CAT). It was assumed that multidimensionality existed in the individuals to whom test items were being administered, i.e., that the correct or incorrect responses given by an individual…
Descriptors: Adaptive Testing, Computer Assisted Testing, Factor Structure, Latent Trait Theory
Kim, Haeok; Plake, Barbara S. – 1993
A two-stage testing strategy is one method of adapting the difficulty of a test to an individual's ability level in an effort to achieve more precise measurement. A routing test provides an initial estimate of ability level, and a second-stage measurement test then evaluates the examinee further. The measurement accuracy and efficiency of item…
Descriptors: Ability, Adaptive Testing, Comparative Testing, Computer Assisted Testing
Allen, Nancy L.; Donoghue, John R. – 1995
This Monte Carlo study examined the effect of complex sampling of items on the measurement of differential item functioning (DIF) using the Mantel-Haenszel procedure. Data were generated using a three-parameter logistic item response theory model according to the balanced incomplete block (BIB) design used in the National Assessment of Educational…
Descriptors: Computer Assisted Testing, Difficulty Level, Elementary Secondary Education, Identification
Kirisci, Levent; Hsu, Tse-Chi – 1992
A predictive adaptive testing (PAT) strategy was developed based on statistical predictive analysis, and its feasibility was studied by comparing PAT performance to those of the Flexilevel, Bayesian modal, and expected a posteriori (EAP) strategies in a simulated environment. The proposed adaptive test is based on the idea of using item difficulty…
Descriptors: Adaptive Testing, Bayesian Statistics, Comparative Analysis, Computer Assisted Testing
Xiao, Beiling – 1990
Dichotomous search strategies (DSSs) for computerized adaptive testing are similar to golden section search strategies (GSSSs). Each middle point of successive search regions is a testing point. After each item is administered, the subject's obtained score is compared with the expected score at successive testing points. If the subject's obtained…
Descriptors: Ability Identification, Adaptive Testing, Computer Assisted Testing, Equations (Mathematics)