An Integrated Corpus-Based Text-Mining Framework for Analyzing Student Discourse

What is it about?

Linguistic landscape (LL) projects are widely used to foster sociolinguistic awareness and experiential learning, yet evaluations have relied predominantly on qualitative analyses of interviews and reflective journals. Consequently, systematic examination of student writing for broader discourse patterns remains limited. This study introduces a corpus-based text-mining framework that reconceptualizes student writing as a research corpus, applying triangulated quantitative analyses to LL project discourse. The corpus comprised discussion and conclusion sections from 95 undergraduate papers written in Japanese by English majors at a Japanese university following neighborhood LL investigations. Using KH Coder, four analytical procedures—word frequency, co-occurrence networks, multidimensional scaling, and hierarchical clustering, identified recurring lexical and conceptual patterns. Findings revealed three main tendencies: students interpreted LLs through functional, tourism-oriented perspectives; employed research-oriented discourse marked by comparison and evidence-based reasoning; and conceptually linked abstract language notions with specific varieties and communicative functions. Beyond substantive insights, the framework offers a scalable, replicable approach for corpus-level analysis that complements qualitative methods, advancing both LL pedagogy and educational text-mining methodologies.

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Photo by Quilia on Unsplash

Photo by Quilia on Unsplash

Why is it important?

Linguistic landscape (LL) projects are widely used to foster sociolinguistic awareness and experiential learning, yet evaluations have relied predominantly on qualitative analyses of interviews and reflective journals. Consequently, systematic examination of student writing for broader discourse patterns remains limited. This study introduces a corpus-based text-mining framework that reconceptualizes student writing as a research corpus, applying triangulated quantitative analyses to LL project discourse. The corpus comprised discussion and conclusion sections from 95 undergraduate papers written in Japanese by English majors at a Japanese university following neighborhood LL investigations. Using KH Coder, four analytical procedures—word frequency, co-occurrence networks, multidimensional scaling, and hierarchical clustering, identified recurring lexical and conceptual patterns. Findings revealed three main tendencies: students interpreted LLs through functional, tourism-oriented perspectives; employed research-oriented discourse marked by comparison and evidence-based reasoning; and conceptually linked abstract language notions with specific varieties and communicative functions. Beyond substantive insights, the framework offers a scalable, replicable approach for corpus-level analysis that complements qualitative methods, advancing both LL pedagogy and educational text-mining methodologies.

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