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.
Index Terms—Linked data, serious game, quiz, authoring tool, e-learning.
Wei Shi and Yoshihiro Okada are with the Innovation Center for Educational Resources, Kyushu University Library, Kyushu University, Fukuoka, Japan (e-mail: shi.wei.243@m.kyushu-u.ac.jp, okada@inf.kyushu-u.ac.jp).
Kosuke Kaneko is with the Cybersecurity Center, Kyushu University, Japan (e-mail: kaneko.kosuke.437@m.kyushu-u.ac.jp).
Chenguang Ma is with the Graduate School of ISEE, Kyushu University, Fukuoka, Japan (e-mail: machenguanglala@yahoo.co.jp).
Cite: Wei Shi, Kosuke Kaneko, Chenguang Ma, and Yoshihiro Okada, "A Framework for Automatically Generating Quiz-Type Serious Games Based on Linked Data," International Journal of Information and Education Technology vol. 9, no. 4, pp. 250-256, 2019.