Structural Gamification for Adaptation based on Learning Analytics - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Structural Gamification for Adaptation based on Learning Analytics


Gamification is widely used to increase learner motivation when using a learning environment. Recent research has shown that gamification should be adapted to learner profile, as learners do not respond at the same manner to game elements, and in some cases, learners can feel demotivated by certain game elements. We propose a structured approach for gamification, named structural gamification that allows adapting game elements without changing the educational content or altering the functioning of the learning environment. This approach relies on three levels of abstraction: motivational strategies (high-level tactics or techniques to encourage learners to participate in the learning process), game elements (specific implementations of motivational strategies), and game element instances (contextualised instantiations of game elements). Using this approach, adaptation can occur on any of the three levels, either by changing motivational strategy, or game element, or by modifying the game element instance directly. This allows for a more fine-grained adaptation that is based on three components: the learner profile, interaction traces with the learning environment, and contextual information. Our future work will be focused on the analysis of learner engagement during the learning activity through their interaction traces to allow for dynamic adaptive gamification.
Fichier principal
Vignette du fichier
EARLI Final.pdf (223.45 Ko) Télécharger le fichier
AbstractionLevels.png (24 Ko) Télécharger le fichier
AdaptationEngine.png (159.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02147867 , version 1 (05-06-2019)


  • HAL Id : hal-02147867 , version 1


Stuart Hallifax, Elise Lavoué, Audrey Serna, Jean-Charles Marty. Structural Gamification for Adaptation based on Learning Analytics. EARLI, Aug 2019, Aachen, Germany. pp.383. ⟨hal-02147867⟩
244 View
293 Download


Gmail Facebook X LinkedIn More