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Okim Kang; Maria Kostromitina; Xun Yan; Ron I. Thomson; Talia Isaacs – Language Assessment Quarterly, 2025
There has been much debate in assessment research about the inclusion of Global English accents in high-stakes listening tests. This study explored test-takers' attitudes toward the inclusion of different English accents in the Duolingo English Test (DET) 2021 test version and their associations with listening test scores. One hundred sixty…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computer Software
Kyle, Kristopher; Eguchi, Masaki; Choe, Ann Tai; LaFlair, Geoff – Language Testing, 2022
In the realm of language proficiency assessments, the domain description inference and the extrapolation inference are key components of a validity argument. Biber et al.'s description of the lexicogrammatical features of the spoken and written registers in the T2K-SWAL corpus has served as support for the TOEFL iBT test's domain description and…
Descriptors: Language Variation, Written Language, Speech Communication, Inferences
Kang, Okim; Thomson, Ron; Moran, Meghan – TESOL Quarterly: A Journal for Teachers of English to Speakers of Other Languages and of Standard English as a Second Dialect, 2019
This study examines the effect of incorporating a variety of international English accents into a simulated TOEFL listening comprehension test in growing recognition of internationalization of language teaching and learning in the field of TESOL. Although some high-stakes English proficiency exams have begun incorporating speech samples produced…
Descriptors: Pronunciation, Listening Comprehension Tests, Language Variation, English (Second Language)
Miao, Yongzhi – Language Testing, 2023
Scholars have argued for the inclusion of different spoken varieties of English in high-stakes listening tests to better represent the global use of English. However, doing so may introduce additional construct-irrelevant variance due to accent familiarity and the shared first language (L1) advantage, which could threaten test fairness. However,…
Descriptors: Pronunciation, Metalinguistics, Native Language, Intelligibility
Hannah, L.; Kim, H.; Jang, E. E. – Language Assessment Quarterly, 2022
As a branch of artificial intelligence, automated speech recognition (ASR) technology is increasingly used to detect speech, process it to text, and derive the meaning of natural language for various learning and assessment purposes. ASR inaccuracy may pose serious threats to valid score interpretations and fair score use for all when it is…
Descriptors: Task Analysis, Artificial Intelligence, Speech Communication, Audio Equipment
Kang, Okim; Rubin, Don; Kermad, Alyssa – Language Testing, 2019
As a result of the fact that judgments of non-native speech are closely tied to social biases, oral proficiency ratings are susceptible to error because of rater background and social attitudes. In the present study we seek first to estimate the variance attributable to rater background and attitudinal variables on novice raters' assessments of L2…
Descriptors: Evaluators, Second Language Learning, Language Tests, English (Second Language)
Wei, Jing; Llosa, Lorena – Language Assessment Quarterly, 2015
This article reports on an investigation of the role raters' language background plays in raters' assessment of test takers' speaking ability. Specifically, this article examines differences between American and Indian raters in their scores and scoring processes when rating Indian test takers' responses to the Test of English as a Foreign…
Descriptors: North Americans, Indians, Evaluators, English (Second Language)