A web-based Chinese handwriting education system with automatic feedback and analysis

Kai-Tai Tang, Ka-Ki Li, Howard Leung

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

25 Citations (Scopus)

Abstract

We propose a web-based Chinese handwriting system that allows students to learn Chinese handwriting at anytime and anywhere. The system combines technologies such as electronic ink, database, Flash animations, and pattern recognition technique to provide students with an intelligent and effective learning environment. In particular, our system contains an Automatic Feedback and Analysis (AFA) tool that can check multiple kinds of handwriting mistakes and provide useful feedback to the user. Survey results show that most users agreed that our proposed system can help them to learn Chinese handwriting more efficiently as well as reducing teachers' workload. The user evaluation results suggest that it takes less time for users to learn Chinese handwriting with our system than with traditional teaching method. © Springer-Verlag Berlin Heidelberg 2006.
Original languageEnglish
Title of host publicationAdvances in Web Based Learning -- ICWL 2006
Subtitle of host publication5th International Conference, Penang, Malaysia, July 19-21, 2006, Revised Papers
EditorsWenyin Liu, Qing Li, Rynson W.H. Lau
Place of PublicationBerlin, Heidelberg
PublisherSpringer 
Pages176-188
ISBN (Electronic)978-3-540-68509-8
ISBN (Print)9783540490272
DOIs
Publication statusPublished - 2006
Event5th International Conference on Web Based Learning (ICWL 2006) - Penang, Malaysia
Duration: 19 Jul 200621 Jul 2006

Publication series

NameLecture Notes in Computer Science
Volume4181
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Web Based Learning (ICWL 2006)
PlaceMalaysia
CityPenang
Period19/07/0621/07/06

Research Keywords

  • Automatic feedback and analysis
  • Chinese handwriting education
  • Stroke production
  • Stroke sequence
  • Web-based learning

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