Register Variation in Spoken and Written Language Use across Technology-Mediated and Non-Technology-Mediated Learning Environments

Autor/inn/en	Kyle, Kristopher; Eguchi, Masaki; Choe, Ann Tai; LaFlair, Geoff
Titel	Register Variation in Spoken and Written Language Use across Technology-Mediated and Non-Technology-Mediated Learning Environments
Quelle	In: Language Testing, 39 (2022) 4, S.618-648 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kyle, Kristopher) ORCID (LaFlair, Geoff)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0265-5322
DOI	10.1177/02655322211057868
Schlagwörter	Language Variation; Written Language; Speech Communication; Inferences; Second Language Learning; English (Second Language); Language Tests; Language Proficiency; Test Validity; Grammar; Computational Linguistics; Computer Assisted Testing; Computer Assisted Instruction; Language Usage; Higher Education; Vocabulary Skills; MOOCs; English for Academic Purposes; Test of English as a Foreign Language + Suchen Sie Ihr Suchwort? Sprachenvielfalt; Geschriebene Sprache; Inference; Inferenz; Zweitsprachenerwerb; English as second language; English; Second Language; Englisch als Zweitsprache; Language test; Sprachtest; Language skill; Language skills; Sprachkompetenz; Testvalidität; Grammatik; Linguistics; Computerlinguistik; Computer based training; Computerunterstützter Unterricht; Sprachgebrauch; Hochschulbildung; Hochschulsystem; Hochschulwesen; Aktiver Wortschatz
Abstract	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 extrapolation inferences. In the time since the T2K-SWAL corpus was collected, however, university learning environments have increasingly become technology-mediated. Accordingly, any description of the linguistic features of university language should account for the language produced in technology-mediated learning environments (TMLEs) in addition to non-technology-mediated learning environments (non-TMLEs). Kyle et al. recently began to address this issue by collecting a corpus of TMLE language use, which they then compared to language use in non-TMLEs using multidimensional analysis (MDA). The results indicated both similarities and substantive differences across the learning environments, but the study did not investigate the effects of particular registers on these results. In this study, we build on previous research by investigating lexicogrammatical features of specific spoken and written registers across technology-mediated and non-technology-mediated learning environments. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01