Multi-scenario modelling of learning

International audienceDesigning an educational scenario is a sensitive and challenging activity because it is the vector of learning. However, the designed scenario may not correspond to some learners’ characteristics (pace of work, cognitive styles, emotional factors, prerequisite knowledge, …). To personalize the learning task and adapt it gradually to each learner, several scenarios are needed. Adaptation and personalization are difficult because it is necessary on the one hand to know in advance the profiles and on the other hand to produce the multiple scenarios corresponding to these profiles. Our model allows to design many scenarios without knowing the learner profiles beforehand. Furthermore, it offers each learner opportunities to choose a scenario and to change it during their learning process. The model ensures that all announced objectives have enough resources for acquiring knowledge and activities for evaluation

To tailor or not to tailor gamification? An analysis of the impact of tailored game elements on learners’ behaviours and motivation

International audienceGamification, defined as the use of game elements in non game situations, is a widely used method to foster learner engagement and motivation. It is generally accepted that in order to be effective, gamification should be tailored to users. Currently, most systems adapt by assigning different game elements based on a single learner profile (e.g. dominant player type, personality or gender). However, there is no study yet that analyse the effect of combining several profiles. In this paper, we study the usage data from 258 students who used a gamified learning environment as a part of their mathematics class. By simulating different adaptation techniques, we show that the learner model chosen to tailor gamification has significant effects on learners' motivation and engaged behaviours depending on the profile(s) used in this context. We also show that tailoring to initial motivation to learn mathematics can improve intrinsic motivation. Finally, we show that tailoring to both player type and motivation profiles can improve intrinsic motivation, and decrease amotivation, compared to a single adaptation only based on learner motivation. We discuss the implications of our findings regarding the choice of a learner model for tailoring gamification in educational environments

A player model for adaptive gamification in learning environments

International audienceMany learning environments are swiftly abandoned by the learners, even if they are effective. Gamification is as a recent game-based learning approach that can enhance the learners’ motiva-tion. However, individual expectations and preferences towards game-like features may be very different from one person to another. This paper presents a model to adapt gamification features according to a player profile of the learners. Two version of this model are evaluated within a gamified online learning environment. The first version comes from experts’ judgment, and the second one is induced from empirical data. Our experiments confirm that the first version can be efficient to predict the player’s preferences among the gamification features

MAGAM: A Multi-Aspect Generic Adaptation Model for Learning Environments

International audienceAdaptation in learning environments can be performed according to various aspects, such as didactics, pedagogy or game mechanics. While most current approaches propose to adapt according to a single aspect, this paper proposes a Multi-Aspect Generic Adaptation Model (MAGAM). Based on the Q-matrix, this model aims at taking into account heterogeneous data to select adapted activities. It has been implemented and used into an experiment which allowed the adaptation of learning activities for 97 students based on both knowl‐ edge and gaming profiles. This experiment has shown the usefulness of MAGAM to combine various aspects of adaptation in ecological conditions