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Peter Baldwin; Victoria Yaneva; Kai North; Le An Ha; Yiyun Zhou; Alex J. Mechaber; Brian E. Clauser – Journal of Educational Measurement, 2025
Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…
Descriptors: Artificial Intelligence, Scoring, Computational Linguistics, Accuracy
Han, Suhwa; Kang, Hyeon-Ah – Journal of Educational Measurement, 2023
The study presents multivariate sequential monitoring procedures for examining test-taking behaviors online. The procedures monitor examinee's responses and response times and signal aberrancy as soon as significant change is identifieddetected in the test-taking behavior. The study in particular proposes three schemes to track different…
Descriptors: Test Wiseness, Student Behavior, Item Response Theory, Computer Assisted Testing
Lim, Hwanggyu; Choe, Edison M. – Journal of Educational Measurement, 2023
The residual differential item functioning (RDIF) detection framework was developed recently under a linear testing context. To explore the potential application of this framework to computerized adaptive testing (CAT), the present study investigated the utility of the RDIF[subscript R] statistic both as an index for detecting uniform DIF of…
Descriptors: Test Items, Computer Assisted Testing, Item Response Theory, Adaptive Testing
Peer reviewedBirenbaum, Menucha; Tatsuoka, Kikumi K. – Journal of Educational Measurement, 1987
The present study examined the effect of three modes of feedback on the seriousness of error types committed on a post-test. The effect of the feedback made on post-test errors was found to be differential and dependent upon the seriousness of errors committed on the pre-test. (Author/LMO)
Descriptors: Computer Assisted Testing, Error Patterns, Feedback, Junior High Schools
Peer reviewedBejar, Isaac I. – Journal of Educational Measurement, 1984
Approaches proposed for educational diagnostic assessment are reviewed and identified as deficit assessment and error analysis. The development of diagnostic instruments may require a reexamination of existing psychometric models and development of alternative ones. The psychometric and content demands of diagnostic assessment all but require test…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Criterion Referenced Tests, Diagnostic Tests
Peer reviewedTatsuoka, Kikumi K. – Journal of Educational Measurement, 1987
This study examined whether the item response curves from a two-parameter model reflected characteristics of the mathematics items, each of which required unique cognitive tasks. A computer program performed error analysis of test performance. Cognitive subtasks appeared to influence the slopes and difficulties of item response curves. (GDC)
Descriptors: Cognitive Processes, Computer Assisted Testing, Error Patterns, Item Analysis
Peer reviewedWebb, Noreen M.; And Others – Journal of Educational Measurement, 1986
The consistency of student response patterns on a test of language arts was examined in a set of studies as a first step toward designing a computer adaptive test to diagnose errors. (Author/LMO)
Descriptors: Adaptive Testing, Computer Assisted Testing, Diagnostic Tests, Error Patterns

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