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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
J. Dylan Burton – Language Testing, 2024
Nonverbal behavior can impact language proficiency scores in speaking tests, but there is little empirical information of the size or consistency of its effects or whether language proficiency may be a moderating variable. In this study, 100 novice raters watched and scored 30 recordings of test takers taking an international, high stakes…
Descriptors: Nonverbal Ability, Language Fluency, Second Language Learning, Language Proficiency
Chen, Fang; Chalhoub-Deville, Micheline – Language Testing, 2014
Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…
Descriptors: Regression (Statistics), Language Tests, Language Proficiency, Mathematics Achievement
Bridgeman, Brent; Powers, Donald; Stone, Elizabeth; Mollaun, Pamela – Language Testing, 2012
Scores assigned by trained raters and by an automated scoring system (SpeechRater[TM]) on the speaking section of the TOEFL iBT[TM] were validated against a communicative competence criterion. Specifically, a sample of 555 undergraduate students listened to speech samples from 184 examinees who took the Test of English as a Foreign Language…
Descriptors: Undergraduate Students, Speech Communication, Rating Scales, Scoring