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Taichi Yamashita – Language Testing, 2025
With the rapid development of generative artificial intelligence (AI) frameworks (e.g., the generative pre-trained transformer [GPT]), a growing number of researchers have started to explore its potential as an automated essay scoring (AES) system. While previous studies have investigated the alignment between human ratings and GPT ratings, few…
Descriptors: Artificial Intelligence, English (Second Language), Second Language Learning, Second Language Instruction
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Monfils, Lora F.; Manna, Venessa F. – Language Testing, 2021
This study used survival analysis to examine the patterns and factors associated with time to achieving designated score criteria on a test of English as a foreign language. This was modeled using an extension of the Cox regression model, with two criterion score levels defined as achieving a TOEFL iBT® total test scale score at or above the…
Descriptors: Language Tests, English (Second Language), Second Language Learning, Scores
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Barkaoui, Khaled – Language Testing, 2019
This study aimed to examine the sources of variability in the second-language (L2) writing scores of test-takers who repeated an English language proficiency test, the Pearson Test of English (PTE) Academic, multiple times. Examining repeaters' test scores can provide important information concerning factors contributing to "changes" in…
Descriptors: Second Language Learning, Writing Tests, Scores, English (Second Language)
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Yoo, Hanwook; Manna, Venessa F.; Monfils, Lora F.; Oh, Hyeon-Joo – Language Testing, 2019
This study illustrates the use of score equity assessment (SEA) for evaluating the fairness of reported test scores from assessments intended for test takers from diverse cultural, linguistic, and educational backgrounds, using a workplace English proficiency test. Subgroups were defined by test-taker background characteristics that research has…
Descriptors: English (Second Language), Second Language Learning, Culture Fair Tests, Test Validity
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Aryadoust, Vahid; Zhang, Limei – Language Testing, 2016
The present study used the mixed Rasch model (MRM) to identify subgroups of readers within a sample of students taking an EFL reading comprehension test. Six hundred and two (602) Chinese college students took a reading test and a lexico-grammatical knowledge test and completed a Metacognitive and Cognitive Strategy Use Questionnaire (MCSUQ)…
Descriptors: Foreign Countries, College Students, Item Response Theory, Reading Comprehension