Baptiste Monterrat

A Player Model for Adaptive Gamification in Learning Environments

Many 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’ motivation. 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.

Toward an Adaptive Gamification System for Learning Environments

In a learning situation, gamification is a way to motivate learners and enhance their participation to learning activities by adding game elements. But it still pays little attention to the individual differences among learners’ preferences as players. This paper presents a generic and adaptive gamification system that can be plugged on various learning environments. The game elements can be automatically adapted, based on an analysis of the interaction traces. The architecture of the proposed system is based on the separation between the control of the pedagogical elements and the control of the game elements. To support the adaptivity of the game elements, we refer to a user model based on a list of player types. The user model is initialized thanks to the traces of interactions, and finally used to predict which game feature will be relevant for the user.

An Environment to Support Collaborative Learning by Modding

In this paper, we propose an environment to support collaborative modding, as a new way to learn a subject. Modding can be defined as the activity to modify an existing game with dedicated tools. In a constructivist approach, we base our work on the assumption that modding a learning game can help learners to acquire the concepts of the subject concerned. We also think that modding in collaborative settings can help learners both to learn the subject and to learn to collaborate. We first propose a framework to support collaborative modding activities based on four components: the game, the Game Development Kit GDK, contextual discussions and a knowledge map. We then propose an architecture that integrates these components on a unique platform. We finally present the results of a first exploratory study that demonstrates the feasibility and the interest of this approach for learning and the need for integrating collaborative tools in a unique environment.

Adaptation of Gaming Features for Motivating Learners

Background. Many learning environments are quickly deserted by learners, even if they are efficient. Gamification of learning environments is a recent approach used to enhance learners’ motivation and participation. Aim. People have various expectations and react differently faced with specific game mechanics. An important goal lies in automatically adapting game mechanics according to player types. In this article, we study the gaming features that can be adapted in learning environments and the player model that can be used for the adaptation process. We propose an approach that aims to predict to which game mechanics a user is responsive, and to adapt the gaming features of the system according to this information. Methodology. An implementation was released, and evaluated through an quasi-experimental study with 59 middle school students, each one using the learning environment during three 45-minutes sessions. Results. The results validate the implementation of the system and show that the users’ activity can help to predict their profile. The adaptation process did not improve learners’ engagement as expected, but it shows a path for future research toward an adaptive approach for learning environment gamification.