Characterising postgraduate students' corpus query and usage patterns for disciplinary data-driven learning

Peter CROSTHWAITE, Lillian WONG, Joyce CHEUNG

Research output: Journal PublicationsJournal Article (refereed)

Abstract

Data-driven learning (DDL; Johns, 1991), involving students’ hands-on use of corpora for self-guided language learning, is a methodology now increasingly used in many tertiary contexts to enhance the teaching of disciplinary postgraduate thesis writing. However, there are still few studies tracking students’ actual engagement with corpora for DDL. This mixed-methods study reports on the tracking of students’ corpus use via a purpose-built corpus query and data visualisation platform integrated into a large postgraduate disciplinary thesis writing program at a university in Hong Kong. Data on corpus usage history (e.g. times of access, duration of use), query syntax (e.g. query lexis/phraseology and use of wildcards and part-of-speech tags), query function (e.g. frequency lists/distribution, concordance sorting and collocation) and query filters (e.g. searches by faculty, discipline, or thesis section) were collected from 327 students spanning over 11,000 individual corpus queries. The results show significant interdisciplinary and inter-/intra-user trends and variation in the use of particular corpus functions and query syntax adopted by corpus users. Students varied in the type of knowledge (e.g. domain-specific, language-specific) they were accessing, and frequently went beyond the exemplars of the DDL course materials to generate unique queries under their own initiative. Qualitative case study data from three corpus users’ activity logs also show distinctive individual corpus engagement by query frequency and function. These data provide a clearer insight into what students actually do during DDL and the different directions and trajectories that individual users take as a result of DDL. All accompanying DDL tasks are also included as supplementary materials.
Original languageEnglish
Pages (from-to)255-275
JournalReCALL
Volume31
Issue number3
Early online date19 Jun 2019
DOIs
Publication statusPublished - Sep 2019
Externally publishedYes

Fingerprint

Students
learning
student
syntax
Data visualization
language
Sorting
Probability density function
visualization
Data-driven Learning
Hong Kong
Teaching
Trajectories
university
methodology
trend
history

Keywords

  • Corpora
  • Data-driven learning
  • Disciplinary writing
  • English for academic purposes
  • L2 writing

Cite this

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title = "Characterising postgraduate students' corpus query and usage patterns for disciplinary data-driven learning",
abstract = "Data-driven learning (DDL; Johns, 1991), involving students’ hands-on use of corpora for self-guided language learning, is a methodology now increasingly used in many tertiary contexts to enhance the teaching of disciplinary postgraduate thesis writing. However, there are still few studies tracking students’ actual engagement with corpora for DDL. This mixed-methods study reports on the tracking of students’ corpus use via a purpose-built corpus query and data visualisation platform integrated into a large postgraduate disciplinary thesis writing program at a university in Hong Kong. Data on corpus usage history (e.g. times of access, duration of use), query syntax (e.g. query lexis/phraseology and use of wildcards and part-of-speech tags), query function (e.g. frequency lists/distribution, concordance sorting and collocation) and query filters (e.g. searches by faculty, discipline, or thesis section) were collected from 327 students spanning over 11,000 individual corpus queries. The results show significant interdisciplinary and inter-/intra-user trends and variation in the use of particular corpus functions and query syntax adopted by corpus users. Students varied in the type of knowledge (e.g. domain-specific, language-specific) they were accessing, and frequently went beyond the exemplars of the DDL course materials to generate unique queries under their own initiative. Qualitative case study data from three corpus users’ activity logs also show distinctive individual corpus engagement by query frequency and function. These data provide a clearer insight into what students actually do during DDL and the different directions and trajectories that individual users take as a result of DDL. All accompanying DDL tasks are also included as supplementary materials.",
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Characterising postgraduate students' corpus query and usage patterns for disciplinary data-driven learning. / CROSTHWAITE, Peter; WONG, Lillian; CHEUNG, Joyce.

In: ReCALL, Vol. 31, No. 3, 09.2019, p. 255-275.

Research output: Journal PublicationsJournal Article (refereed)

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