Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107294
Title: E-learning design for Chinese classifiers : reclassification of nouns for a novel approach
Authors: Gao, Helena Hong
Keywords: DRNTU::Engineering::Computer science and engineering::Software::Programming languages
DRNTU::Humanities::Language::Chinese
Issue Date: 2011
Source: Gao, H. H. (2011). E-learning design for Chinese classifiers : reclassification of nouns for a novel approach. Communications in computer and information science, 177, 186-199.
Series/Report no.: Communications in computer and information science
Abstract: Chinese classifiers are found to be a category that creates true challenges for second language learners of Chinese to grasp its meaning and use it easily. It is also found that even native speakers of Chinese may lose their competence in using classifiers appropriately after years of living in a non-Chinese speaking community. This paper presents a novel approach in the design of an e-learning tool for Chinese classifier learning and teaching. With this approach, Chinese noun categories are reclassified according to their associations with the types of classifiers. The design is based on both theoretical studies of Chinese classifiers and empirical studies of Chinese classifier acquisition by both children and adults. It allows users to use cognitive strategies to explore and learn with a bottom-up approach the associations of classifiers with nouns.
URI: https://hdl.handle.net/10356/107294
http://hdl.handle.net/10220/25445
URL: http://link.springer.com/chapter/10.1007%2F978-3-642-22383-9_16#page-1
Rights: © 2011 Springer-Verlag Berlin Heidelberg. This is the author created version of a work that has been peer reviewed and accepted for publication by Communications in Computer and Information Science, Springer-Verlag Berlin Heidelberg. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [Article URL: http://link.springer.com/chapter/10.1007%2F978-3-642-22383-9_16#page-1].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:HSS Journal Articles

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