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Educational Video Game Design Using Personalized Learning Scenarios

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)


Educational video games are considered interactive, user-oriented, and motivating learning instruments, allowing delivery of tailored learning experiences. Designing and implementing adaptive and personalized educational video games can become a suitable tool for teachers in student-centric learning. In this context, the present research aims to outline how mini puzzle games adaptable to the individual learner and proactive learning scenarios can help teachers to adopt enhanced strategies for game-based learning, based on students’ personalization and adaptation. The paper presents design approaches for adjusting educational video mazes to both individual and group student models, which are based on personalization and adaptation. It describes how basic learning scenarios can be personalized once at the level of the maze game by selecting puzzles appropriate for a given individual student and, next, inside each of the puzzles. While the personalization of the learning content and its structuring and presentation is realized statically, a dynamic adaptation of both the content difficulty and gameplay is suggested according to observable changes in student properties during playing the game. Both the personalization and adaptation of the maze containing puzzles are specified by XML descriptions, which are used by different software instruments for building the whole game and for analyzing the outcomes of its playing. There are discussed the design principles of personalized maze games allowing teachers and educators to create and apply their educational games.

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The research leading to these results has received funding from the APOGEE project, funded by the Bulgarian National Science Fund, Grant Agreement No. DN12/7/2017.

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Correspondence to Boyan Bontchev .

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Bontchev, B., Antonova, A., Dankov, Y. (2020). Educational Video Game Design Using Personalized Learning Scenarios. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham.

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  • Print ISBN: 978-3-030-58816-8

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