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14 - A Corpus-Based Multidimensional Analysis of EMI University Classroom Discourse

The Case of Singapore

from Part II - Empirical Chapters (Case Studies)

Published online by Cambridge University Press:  14 December 2024

By
Eniko Csomay ,
Reka R. Jablonkai  and
Siew Mei Wu
Edited by
Samantha M. Curle  and
Jack Pun
Samantha M. Curle
Affiliation:
University of Bath
Jack Pun
Affiliation:
City University of Hong Kong
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Summary

English Medium Instruction (EMI) researchers have called for studies that extend our understanding of EMI classroom discourse and the role of language in EMI in general (Dalton-Puffer & Smit, 2013; McKinley & Rose, 2022; Macaro, 2019). Corpus-based analytical frameworks are well-suited to analyze large amounts of naturally occurring language data and thus to provide reliable and verifiable findings about situationally defined language use such as language use in EMI contexts (see also Chapter 9, Author, this volume).

The primary goal of this chapter is to introduce the principles and practices of carrying out an additive multi-dimensional (MD) analysis (Biber, 1988; Berber Sardina et al., 2019) affording an empirically driven comprehensive linguistic analysis of variation in a register that could be applied to an EMI context. Our case study showcases the methodology using 500,000 words of text from the Singapore EMI corpus (SEMIC). Relying on the results of the MD analysis, this chapter also demonstrates how to identify text types via cluster analysis, which could provide additional information about classroom discourse. It will demonstrate show how these advanced quantitative analytical frameworks can be applied to analyze EMI classroom discourse. The chapter will also highlight practical aspects of MD analysis.

Keywords

EMImulti-dimensional analysisclassroom discoursecorpus-based analysisSingapore
Type
Chapter
Information
Researching English Medium Instruction
Quantitative Methods for Students and Researchers
 , pp. 240 - 259
Publisher: Cambridge University Press
Print publication year: 2024

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