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Jonas Flodén – British Educational Research Journal, 2025
This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three…
Descriptors: College Faculty, Artificial Intelligence, Comparative Testing, Scoring
Ozsoy, Seyma Nur; Kilmen, Sevilay – International Journal of Assessment Tools in Education, 2023
In this study, Kernel test equating methods were compared under NEAT and NEC designs. In NEAT design, Kernel post-stratification and chain equating methods taking into account optimal and large bandwidths were compared. In the NEC design, gender and/or computer/tablet use was considered as a covariate, and Kernel test equating methods were…
Descriptors: Equated Scores, Testing, Test Items, Statistical Analysis
Gönülates, Emre – Educational and Psychological Measurement, 2019
This article introduces the Quality of Item Pool (QIP) Index, a novel approach to quantifying the adequacy of an item pool of a computerized adaptive test for a given set of test specifications and examinee population. This index ranges from 0 to 1, with values close to 1 indicating the item pool presents optimum items to examinees throughout the…
Descriptors: Item Banks, Adaptive Testing, Computer Assisted Testing, Error of Measurement
Patrick C. Kyllonen; Amit Sevak; Teresa Ober; Ikkyu Choi; Jesse Sparks; Daniel Fishtein – ETS Research Institute, 2024
Assessment refers to a broad array of approaches for measuring or evaluating a person's (or group of persons') skills, behaviors, dispositions, or other attributes. Assessments range from standardized tests used in admissions, employee selection, licensure examinations, and domestic and international largescale assessments of cognitive and…
Descriptors: Performance Based Assessment, Evaluation Criteria, Evaluation Methods, Test Bias
Gu, Lixiong; Ling, Guangming; Qu, Yanxuan – ETS Research Report Series, 2019
Research has found that the "a"-stratified item selection strategy (STR) for computerized adaptive tests (CATs) may lead to insufficient use of high a items at later stages of the tests and thus to reduced measurement precision. A refined approach, unequal item selection across strata (USTR), effectively improves test precision over the…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Use, Test Items
Ferrao, Maria – Assessment & Evaluation in Higher Education, 2010
The Bologna Declaration brought reforms into higher education that imply changes in teaching methods, didactic materials and textbooks, infrastructures and laboratories, etc. Statistics and mathematics are disciplines that traditionally have the worst success rates, particularly in non-mathematics core curricula courses. This research project,…
Descriptors: Foreign Countries, Computer Assisted Testing, Educational Technology, Educational Assessment
van der Linden, Wim J. – Applied Psychological Measurement, 2006
Two local methods for observed-score equating are applied to the problem of equating an adaptive test to a linear test. In an empirical study, the methods were evaluated against a method based on the test characteristic function (TCF) of the linear test and traditional equipercentile equating applied to the ability estimates on the adaptive test…
Descriptors: Adaptive Testing, Computer Assisted Testing, Test Format, Equated Scores
Rizavi, Saba; Way, Walter D.; Davey, Tim; Herbert, Erin – Educational Testing Service, 2004
Item parameter estimates vary for a variety of reasons, including estimation error, characteristics of the examinee samples, and context effects (e.g., item location effects, section location effects, etc.). Although we expect variation based on theory, there is reason to believe that observed variation in item parameter estimates exceeds what…
Descriptors: Adaptive Testing, Test Items, Computation, Context Effect