Learning by pet-training competition: Alleviating negative influences of direction competition by training pets to compete in game-based environments

Zhi Hong Chen*, C. Y.Liao Calvin, Tak Wai Chan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Competition is a powerful motivator in game-based learning but direct competition might bring negative influences on students when they lose the competition, such as damage to confidence. In this paper, we propose an approach to alleviate such negative influences - learning by pet-training competition, which provides an indirect competition environment to enhance students to learn through training their pets. That is, students learn Chinese idioms in a pet-training game scenario, and their mastery is related to the pets' strength to win the competition. In such environment, the competition result might be regarded as a matter whether students spend enough time in learning. This attribution might alleviate the negative influence that results from the direct competition.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010
Pages411-413
Number of pages3
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010 - Sousse, Tunisia
Duration: 2010 Jul 52010 Jul 7

Publication series

NameProceedings - 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010

Other

Other10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010
Country/TerritoryTunisia
CitySousse
Period2010/07/052010/07/07

Keywords

  • Competition
  • Game-based learning
  • Virtual pets

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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