publication . Part of book or chapter of book . 2016

A scientific methodology for researching CALL interaction data: Multimodal LEarning and TEaching Corpora

Multimodal LEarning and TEaching Corpora
Thierry Chanier; Ciara R. Wigham;
Open Access English
  • Published: 13 Jun 2016
  • Publisher: HAL CCSD
  • Country: France
International audience; 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.
Persistent Identifiers
Fields of Science and Technology classification (FOS)
05 social sciences, 0503 education, 050301 education, 06 humanities and the arts, 0602 languages and literature, 060201 languages & linguistics
free text keywords: LEarning and TEaching Corpora (LETEC), staged methodology, multimodal transcription, OpenData, [SHS.EDU]Humanities and Social Sciences/Education, Data science, Scientific object, Artificial intelligence, business.industry, business, Multimodal learning, Corpus linguistics, Structuring, Natural language processing, computer.software_genre, computer, Perspective (graphical), Online learning, Computer science, Field (computer science), Transcription (linguistics)
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