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Alexander James Kwako – ProQuest LLC, 2023
Automated assessment using Natural Language Processing (NLP) has the potential to make English speaking assessments more reliable, authentic, and accessible. Yet without careful examination, NLP may exacerbate social prejudices based on gender or native language (L1). Current NLP-based assessments are prone to such biases, yet research and…
Descriptors: Gender Bias, Natural Language Processing, Native Language, Computational Linguistics
Bogorevich, Valeriia – ProQuest LLC, 2018
Rater variation in performance assessment can impact test-takers' scores and compromise assessments' fairness and validity (Crooks, Kane, & Cohen, 1996). Rater variation can also undermine a test's validity and fairness; therefore, it is important to investigate raters' scoring patterns in order to inform rater training. Substantial work has…
Descriptors: Pronunciation, Familiarity, English (Second Language), Second Language Learning
Brooks, Rachel Lunde – ProQuest LLC, 2013
Previous Language Testing research has largely reported that although many raters' characteristics affect their evaluations of language assessments (Reed & Cohen, 2001), being a native speaker or non-native speaker rater does not significantly affect final ratings (Kim, 2009). In Second Language Acquisition, some researchers conclude that…
Descriptors: Comparative Analysis, Second Language Learning, Native Speakers, Language Proficiency