A framework for automatically generating quiz-type serious games based on linked data

Research output: Contribution to journalArticle

Abstract

Quiz-type serious games are widely used not only for evaluating quiz users’ learning effects but also for supporting quiz users’ learning activities. However, we suppose that quiz games created by the traditional method have some demerits. First, the storage of the similar quiz questions is extravagant. Second, the choices for each quiz question almost have no or only few changes. Furthermore, current quizzes do not effectively analyze users’ activities. For solving the above problems, we propose a new framework which supports to easily create the customized quiz games. The quiz resources are stored as the Linked Data. The linkages among data makes the automatic generation of the choices of each quiz question become possible. Such kind of quiz generation method can realize the wrong choices of the same question are different for each time. Our framework includes two tools. One is to extract and visually represent the schema of the Linked Data. The other is an authoring tool for supporting quiz makers to define a template of quiz pages. The quizzes generated by our framework can collect quiz users’ feedbacks and record the users’ activities and scores. These collected data will be used for the further analyzation.

Original languageEnglish
Pages (from-to)250-256
Number of pages7
JournalInternational Journal of Information and Education Technology
Volume9
Issue number4
DOIs
Publication statusPublished - Apr 2019

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All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications

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