Making the real world as a game world to learners by applying game-based learning scenes into ubiquitous learning environment

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Abstract

A ubiquitous learning environment provides learners opportunities of observing and touching the learning objects around the learners according to their preferences and/or interests. Learners can solve problems, answer questions, and propose their own questions, in the ubiquitous learning environment. However, it is still a serious topic to learners and most of them only do learning when they really have to, because it is a learning task. Games have many features which make learners have higher motivation in learning and have willing in learning actively. Moreover, the 'stage' concept in the games is also a big factor which might attract learners doing continuous learning, because the learners feel they really accomplished something and want to challenge further. In this paper, we first use small stories and activities to figure out what the learners interests with, which means, make learners call; and, build the personalized knowledge structure for the learner according to his/her choices and interests. With the helps of the context-awareness knowledge structure, we designs and builds learning scenes to offer learners the personalized learning services based on the 'stage' concept in the game. Each learning scene may cover one or many learning spots, and each learning spot has different learning objects. We can construct a series of learning scenes dynamically for individual learner based on the learner's choices, preferences and interests. Furthermore, a learning path involves learning scene switching is also generated automatically for the learner. A scenario with several exhibition rooms and artifacts in a museum is used to demonstrate the idea and mechanism proposed by this research. © 2008 Springer-Verlag Berlin Heidelberg.

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APA

Chang, M., Wu, S., & Heh, J. S. (2008). Making the real world as a game world to learners by applying game-based learning scenes into ubiquitous learning environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5080 LNCS, pp. 261–277). https://doi.org/10.1007/978-3-540-69744-2_20

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