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Part of book or chapter of book . 2016 . Peer-reviewed
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A scientific methodology for researching CALL interaction data

Multimodal LEarning and TEaching Corpora
Authors: Chanier, Thierry; Wigham, Ciara;

A scientific methodology for researching CALL interaction data

Abstract

This chapter gives an overview of one possible staged methodology for structuring LCI data by presenting a new scientific object, LEarning and TEaching Corpora (LETEC). Firstly, the chapter clarifies the notion of corpora, used in so many different ways in language studies, and underlines how corpora differ from raw language data. Secondly, using examples taken from actual online learning situations, the chapter illustrates the methodology that is used to collect, transform and organize data from online learning situations in order to make them sharable through open-access repositories. The ethics and rights for releasing a corpus as OpenData are discussed. Thirdly, the authors suggest how the transcription of interactions may become more systematic, and what benefits may be expected from analysis tools, before opening the CALL research perspective applied to LCI towards its applications to teacher-training in Computer-Mediated Communication (CMC), and the common interests the CALL field shares with researchers in the field of Corpus Linguistics working on CMC.

International audience

Country
France
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Scientific object computer.software_genre Structuring Field (computer science) Transcription (linguistics) Corpus linguistics business.industry Online learning Perspective (graphical) Data science Multimodal learning Artificial intelligence business computer Natural language processing

Keywords

[SHS.EDU]Humanities and Social Sciences/Education, multimodal transcription, OpenData, LEarning and TEaching Corpora (LETEC), staged methodology

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average