Chinese segmentation and collocation: A platform for blended learning

Research output: Chapter in Book/Report/Conference proceedingChapter

84 Citations (Scopus)
18 Downloads (Pure)

Abstract

Mandarin Chinese is an increasingly popular world language and object of study, and while there are numerous online character learning apps and flashcard systems, very little research has been done on inductive or autonomous learning in the realm of collocation acquisition. I propose a new Chinese implementation of a trusted corpus-based platform, currently available for English and several other languages, accompanied and enhanced by an adaptive approach to Chinese segmentation approach, whereby different ways of carving up a given sentence are selectively displayed to the learner.
Original languageEnglish
Title of host publicationFlipping the blend through MOOCs, MALL and OIL – new directions in CALL
EditorsMarina Orsini-Jones, Simon Smith
Place of PublicationVoillans, France
PublisherResearch-publishing.net
Chapter8
Pages59-66
Number of pages8
ISBN (Electronic)978-2-490057-17-7
ISBN (Print)978-2-490057-16-0
DOIs
Publication statusPublished - 13 Jun 2018

Bibliographical note

This volume is published under the Attribution-NonCommercial-NoDerivatives International (CC BY-NC-ND) licence; individual articles may have a different licence. Under the CC BY-NC-ND licence, the volume is freely available online (https://doi.org/10.14705/rpnet.2018.23.9782490057160) for anybody to read, download, copy, and redistribute provided that the author(s), editorial team, and publisher are properly cited. Commercial use and derivative works are, however, not permitted.

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  • Research Output

    • 84 Citations
    • 1 Anthology or Edited Book
    Open Access
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  • 119 Downloads (Pure)

    Cite this

    Smith, S. (2018). Chinese segmentation and collocation: A platform for blended learning. In M. Orsini-Jones, & S. Smith (Eds.), Flipping the blend through MOOCs, MALL and OIL – new directions in CALL (pp. 59-66). Voillans, France: Research-publishing.net. https://doi.org/10.14705/rpnet.2018.23.791