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Article published In:
Journal of Immersion and Content-Based Language Education
Vol. 7:1 (2019) ► pp.4–29

Capturing technical terms in spoken CLIL

A holistic model for identifying subject-specific vocabulary

| University of Vienna
| University of Vienna
| University of Vienna

Content and language integrated learning (CLIL), an educational approach using a foreign language to teach non-language subjects, has been consistently gaining in popularity. Despite an increasing research base suggesting its benefits for general language proficiency, the contribution made to learning and using subject-specific target language elements is largely under-researched. This paper addresses one aspect of this, i.e. students’ use of subject-specific vocabulary in CLIL classroom communication. We propose a holistic model for identifying both single and multi-word lexical units specific to the school subject in oral classroom data, integrating corpus-linguistic and qualitative data analysis. The method is trialled using a data set of 16 hours of secondary-school CLIL classroom data within the subject of European economics and politics in Year 12. Findings show that a holistic definition of subject-specific vocabulary is vital, and that the model constitutes an adequate and flexible tool for specifying CLIL terminology in oral classroom discourse.

Keywords: L2 lexicon, technical vocabulary, subject-specific language, upper-secondary school, corpus linguistics
Article outline
  • 1.Introduction
  • 2.CLIL and subject-specific vocabulary
    • 2.1The notion of subject-specific vocabulary
    • 2.2Previous research on subject-specific vocabulary in CLIL
    • 2.3Insights from related disciplines
  • 3.A holistic 3-phase model for determining subject-specific vocabulary in oral CLIL-data
    • 3.1Phase 1: Automatized quantitative analysis: Keyword & cluster analysis
    • 3.2Phase 2: Qualitative selection through researchers (rating scale steps 1&2)
    • 3.3Phase 3: Final selection involving experts (rating scale steps 3&4)
  • 4.Trialling the 3-phase model
    • 4.1Data set
    • 4.2Conducting the SSV analysis
    • 4.3SSV in the CLIL-data set
  • 5.Strengths and limitations of the model
  • 6.Conclusion
  • Notes
  • References
Published online: 28 February 2019
DOI logo
https://doi.org/10.1075/jicb.17029.rie
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