Register variation in spoken and written language use across technology-mediated and non-technology-mediated learning environments

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.

Evaluating evidence for the reliability and validity of lexical diversity indices in L2 oral task responses

“…These results diverge from those of previous L2 writing studies (Alexopoulou et al, 2017;Yoon, 2017;Zenker & Kyle, 2021) that found systematic differences across written task types and task prompts. It is possible that less variation was found across spoken task responses than in written ones because spoken texts tend to have a higher proportion of (repeated) function words, whereas written texts tend to have a higher density of content words (e.g., Biber et al, 2004;Kyle et al, 2022;Read, 2000). Function word repetition may smooth out differences in content word use across tasks.…”

A Multidimensional Analysis of a High-Stakes English Listening Test: A Corpus-Based Approach

“…Following Kyle et al [14,15] and Biber [10,55], the researchers conducted a multidimensional analysis to reduce the variables that were generated by the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC; [56]) into latent factors, interpreted the factors as dimensions of language variation, and subsequently computed dimension scores and compared the scores of each dimension across different listening types and time periods. The Jamovi statistical package [57] was chosen as the tool for data processing and comparison.…”

“…With regard to language assessment, a detailed account of the characteristics of a certain language register may offer strong evidence to support the inference of domain descriptions in assessments [11]. This is the reason why MDA has been adopted to investigate features of written or oral texts produced by test-takers [1,12,13] or to examine the overall resemblance of language used in assessments with target language use domains [14][15][16]. MDA has been widely used to provide evidence for both written and spoken tests' validity argument through comparing the similarities and differences between test language registers and TLU domains.…”

Measuring the development of lexical richness of L2 Spanish: A longitudinal learner corpus study

“…Spoken language differs from written language in that it generally involves less planning and lack of editing, especially in interpersonal communication. Linguistic features of spoken and written language are influenced not only by mode but also by register (Biber, 1988;Biber & Conrad, 2019;Kyle et al, 2022). Interpersonal registers vary in their situational context (e.g., everyday conversation versus office hours), which is also characterized by different linguistic features (Biber & Conrad, 2019).…”

“…Interpersonal registers vary in their situational context (e.g., everyday conversation versus office hours), which is also characterized by different linguistic features (Biber & Conrad, 2019). Less formal registers tend to be characterized by less sophisticated lexical items (though these items may still be diverse; Biber et al, 2004;Kyle et al, 2022). Consequently, research investigating spontaneous spoken data has found that more proficient learners tend to produce more frequent words (Crossley et al, 2011a;Eguchi & Kyle, 2020;Kyle & Crossley, 2015).…”