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Hong Jiao, Editor; Robert W. Lissitz, Editor – IAP - Information Age Publishing, Inc., 2024
With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructured data source may bring new perspectives to better…
Descriptors: Artificial Intelligence, Natural Language Processing, Psychometrics, Computer Assisted Testing
Shermis, Mark D.; Mao, Liyang; Mulholland, Matthew; Kieftenbeld, Vincent – International Journal of Testing, 2017
This study uses the feature sets employed by two automated scoring engines to determine if a "linguistic profile" could be formulated that would help identify items that are likely to exhibit differential item functioning (DIF) based on linguistic features. Sixteen items were administered to 1200 students where demographic information…
Descriptors: Computer Assisted Testing, Scoring, Hypothesis Testing, Essays
Zhang, Mo; Breyer, F. Jay; Lorenz, Florian – ETS Research Report Series, 2013
In this research, we investigated the suitability of implementing "e-rater"® automated essay scoring in a high-stakes large-scale English language testing program. We examined the effectiveness of generic scoring and 2 variants of prompt-based scoring approaches. Effectiveness was evaluated on a number of dimensions, including agreement…
Descriptors: Computer Assisted Testing, Computer Software, Scoring, Language Tests
Douglas, Dan, Ed.; Chapelle, Carol, Ed. – 1993
Papers from the conference on language testing include: "Foundations and Directions for a New Decade of Language Testing" (Carol Chapelle, Dan Douglas); "A Comparison of the Abilities Measured by the Cambridge and Educational Testing Service EFL Test Batteries" (Lyle F. Bachman, Fred Davidson, John Foulkes); "Judgments in…
Descriptors: Comparative Analysis, Computer Assisted Testing, Diagnostic Tests, Educational Trends