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.

在语言能力评估领域，领域描述推理和外推推理是有效性论证的关键组成部分。Biber 等人对 T2K-SWAL 语料库中口语和书面语的词汇语法特征的描述为 TOEFL iBT 考试的领域描述和外推推理提供了支持。然而，自收集 T2K-SWAL 语料库以来，大学学习环境越来越以技术为媒介。因此，任何对大学语言语言特征的描述都应该考虑到在技术中介学习环境（TMLEs）中产生的语言，以及非技术中介学习环境（non-TMLEs）。凯尔等人。最近开始通过收集 TMLE 语言使用的语料库来解决这个问题，然后，他们使用多维分析 (MDA) 将其与非 TMLE 中的语言使用进行了比较。结果表明了学习环境之间的相似性和实质性差异，但该研究没有调查特定寄存器对这些结果的影响。在这项研究中，我们通过调查技术介导和非技术介导的学习环境中特定口语和书面语域的词汇语法特征来建立之前的研究。