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Dynamic gamification adaptation framework based on engagement detection through learning analytics

Abstract : Most current adaptive gamification approaches use what is often called a "static" adaptation approach-i.e. game elements are adapted to users once, generally before using the gamified tool, based on a static user profile. On the other hand, dynamic adaptation proposes to adapt game elements based on user behaviour in real time, reacting to variations in user engagement. In this paper, we propose an adaptation framework using an initial static adaptation based on learner profiles, and a dynamic adaptation that uses learning analytics to refine the static adaptation recommendations. The adaptation system is able to observe various learning analytics to estimate learner engagement, to compare to that of other learners, and then to signal to teachers learners that require a change in their gamified environment. We propose a protocol for a future study to test our approach in real conditions, and provide some recommendations for future directions.
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https://hal.archives-ouvertes.fr/hal-03196746
Contributeur : Stuart Hallifax <>
Soumis le : mardi 13 avril 2021 - 10:29:08
Dernière modification le : mardi 1 juin 2021 - 14:08:10

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  • HAL Id : hal-03196746, version 1

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Stuart Hallifax, Audrey Serna, Jean-Charles Marty, Elise Lavoué. Dynamic gamification adaptation framework based on engagement detection through learning analytics. Companion Proceedings of the 11th International Conference on Learning Analytics & Knowledge LAK21, 2021. ⟨hal-03196746⟩

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