Gamification, the use of game elements in non game contexts, is becoming widely used in the educational field to enhance learner engagement, motivation, and performance. Many current approaches propose systems where learners use the same game elements. However, recent studies show that learners react differently to different game elements, and that learner motivation, engagement, and performance can vary greatly depending on individual characteristics such as personality, game preferences, and motivation for the learning activity. Results indicate that in some cases game elements that are not adapted to learners can at best fail to motivate them, and at worst demotivate them. Therefore, adapting game elements to individual learner preferences is important. This thesis was part of the LudiMoodle project, dedicated to the gamification of learning resources to enhance learner engagement and motivation. In this thesis, I propose a new system that adapts relevant game elements to learners using individual characteristics, as well as learner engagement. This work is based on previous results in the general gamification field, as well as more specific results from gamification in education. Our main goal is to propose a generic adaptation engine model, instantiated with specific adaptation rules for our educational context.

This manuscript presents four major contributions: (1) A general adaptation engine architecture that can be implemented to propose relevant game elements for learners, using both a static and dynamic adaptation approach; (2) A design space and design tools that allows the creation of relevant and meaningful game elements, in collaboration with the various actors of the gamification process (designers, teachers, learners etc.); (3) A static adaptation approach that uses a compromise between both learners' player profile (i.e. preferences for games) and their initial motivation for the learning task; (4) A dynamic learner model built on a trace-based approach to propose an adaptation intervention when an abnormal decrease in engagement is detected.

The adaptation engine was implemented in a prototype for the LudiMoodle project, that was used by 258 learners in 4 different secondary schools in France for learning mathematics. To build this prototype we ran a real world study, where learners used this tool as a part of their normal mathematics course. From this study, we ran multiple analyses to better understand the factors that influence the motivational variations of the learners, and how their interaction traces could predict their engagement with the learning task. These analyses served to evaluate the impact of the adaptation of game elements on learner motivation and engagement, and to build the trace based model used for dynamic adaptation.

This work represents a significant advancement for the adaptive gamification field, through a generic model for static and dynamic adaptation, with the former based on individual learner characteristics, and the latter on observed learner engagement. I also provide tools and recommendations for designers, to help explore different game element designs. Finally, I discuss these findings in terms of research perspectives, notably with regards to further possible advancements in the dynamic adaptation domain.