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dc.contributor.authorTornqvist, Dominicus
dc.contributor.authorWen, Lian
dc.contributor.authorTichon, Jennifer
dc.contributor.editorDias, N
dc.contributor.editorDeFreitas, S
dc.contributor.editorDuque, D
dc.contributor.editorRodrigues, N
dc.contributor.editorWong, K
dc.contributor.editorVilaca, JL
dc.date.accessioned2017-09-14T06:17:31Z
dc.date.available2017-09-14T06:17:31Z
dc.date.issued2017
dc.identifier.issn2330-5649
dc.identifier.doi10.1109/SeGAH.2017.7939281
dc.identifier.urihttp://hdl.handle.net/10072/346313
dc.description.abstractHow can one measure the learning outcome of playing a serious game? We need an objective measure of the learning contents included in a game. The research is diverse, utilizing vastly different games to teach various different kinds of knowledge and skills. This makes it difficult to compare and generalize studies, lacking any established formal tool of analysis. This problem requires the design of an abstract and objective measurement of the quantity of learning material independent of the learning domain. Based on cognitive research on causal Bayes nets (CBNs), this paper proposes using dynamic causal nets (DCNs) to model an abstract knowledge base, which could be mapped to many different domains of learning. We also apply Kolmogorov Complexity (KC) as an approach to measure the content of the abstract knowledge base. This work will establish a theoretical foundation for future research of serious games.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencename5th IEEE International Conference on Serious Games and Applications for Health (SeGAH)
dc.relation.ispartofconferencetitle2017 IEEE 5TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH (SEGAH)
dc.relation.ispartofdatefrom2017-04-02
dc.relation.ispartofdateto2017-04-04
dc.relation.ispartoflocationPerth, AUSTRALIA
dc.subject.fieldofresearchLearning Sciences
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchcode130309
dc.subject.fieldofresearchcode089999
dc.titleUniversal tools for measuring games and learning: Dynamic causal nets
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorWen, Larry
gro.griffith.authorTornqvist, Dominicus P.
gro.griffith.authorTichon, Jennifer G.


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