QuizMASter – A Multi-Agent Game-Style Learning Activity
Lin, Fuhua (Oscar)
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This paper describes a research project in progress of developing a Multi-Agent System-based educational game QuizMASter for e-learning that would help students learn their course material through friendly competition. We explore the use of perceptive pedagogical agents that would be able to determine the learner’s attitudes; to assess learners’ emotional states through examining learner’s standing, response timing, and history, and banter; and to provide appropriate feedback to students in order to motivate them.