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- W4313444307 abstract "Use of serious games for teaching various important concepts including cybersecurity has been on rise. However, serious games with fixed trajectories tend to be less effective in learning outcomes as this approach of one size fits all fails to hold attention of players with different background knowledge and learning abilities. To address this important gap, we attempt to classify the players, based on initial game play data, in two classes, viz. beginner and expert. The two games, that we describe in this paper, dynamically adapt the teaching and testing phase of the game appropriate to the player type. This feature of the games is expected to suitably adapt the difficulty level of the game to ensure that both beginner and expert players are given appropriate challenge through the game and thus provide the crucial flow towards achieving pedagogical objectives. We also attempt to validate these games using both objective and subjective measures through use of recorded player data and embedded questionnaire on various aspects of user experience (UX). The validation process, leading to a measure of learning outcome, is expected to facilitate suitable improvements to the game design and thus achievement of pedagogical objective while maintaining a sense of play to the potential learner." @default.
- W4313444307 created "2023-01-06" @default.
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- W4313444307 date "2023-01-01" @default.
- W4313444307 modified "2023-10-16" @default.
- W4313444307 title "Adaptive Serious Games to Teach Cybersecurity Concepts Using a Machine Learning Approach" @default.
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- W4313444307 doi "https://doi.org/10.1007/978-981-19-2358-6_35" @default.
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