Universal tools for measuring games and learning: Dynamic causal nets
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How 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.
2017 IEEE 5th International Conference on Serious Games and Applications for Health (SeGAH)
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