Amel Yessad

SeGAE: A Serious Game Authoring Environment

Game-based learning or serious game is becoming an important trend in e-learning research area because it seems address several typical e-learning problems such as high dropout rates due to frustration and the lack of motivation to continue studying and the cognitive overload of the learner. However, an important problem of serious games is the difficulty for instructors to adapt the storyboard, the scripts and the game levels of the videogame to new pedagogical objectives once the game development is achieved. This paper presents SeGAE, an author-friendly environment that offers to instructors a set of editors in order to modify the game design by defining new characters, objectives, victory conditions, authorised actions among other objects in the serious games even after the development stage. Particularly, we apply our authoring approach on Blossom Flowers, a serious game developed by Ktm Advance.

Petri Nets and Ontologies: Tools for the "Learning Player" Assessment in Serious Games

Serious games are now an increasingly used tool in business training. The question of the effectiveness of such devices on learning is a research issue. The indicators provided at the end of a video game are insufficient to understand and follow the path of a learning player. It is therefore necessary to track not only the players actions but also to provide tools in order to analyze and diagnose the knowledge acquisition of the learner. We developed an approach based on Petri nets, used to model the accurate behavior of the player. We complete this tool with ontology to explain learners mistakes.

Using the Petri Nets for the Learner Assessment in Serious Games

Game-based learning or serious games is becoming an important trend in the e-learning research area and seems address several typical e-learning problems such as high dropout rates, due to the lack of motivation to continue studying. In serious games, it is very hard to define and mix the learning situations with the game characteristics, and to integrate an assessment and guidance process of the learner without disturbing the game progress and maintain the intrinsic characteristics of the video game: fun, player motivation, immersion and interaction. In this paper, we consider the serious game as an asynchronous and concurrent system, and we propose an approach based on a Petri net to assess learners and detect misconceptions. In the game design stage, a discussion between domain experts, learning experts, and game designers is engaged in order to identify the actions in the game that imply knowledge acquisition and allow achieving the learning objectives of levels. Therefore, in our approach, the Petri net models only game actions allowing the learner to acquire knowledge. We use the reachability graph of the Petri net to track the learner in order to detect, in real time, the learner’s misconceptions, improve learner assessment and provide an accurate feed back for both the learner and the instructor.

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

Adaptation 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 knowledge and gaming profiles. This experiment has shown the usefulness of MAGAM to combine various aspects of adaptation in ecological conditions.

Adapting Learning Paths in Serious Games: An Approach Based on Teachers' Requirements

Adapting Learning Paths in Serious Games (SGs) is a challenging problem. Indeed, learners are not alike; they have different range of abilities, competences, needs and interests. A well-fitting approach to create adaptive SGs is based on Competence-based Knowledge Space Theory (CbKST). CbKST allows sequencing the SG activities according to knowledge and competences of a domain model, and adaptation is based on suggesting activities that improve learners’ competences. However, differences among learners and the diversity of learning situations may drive teachers to consider implementing different adaptive approaches that fulfil their needs. In this work, we propose to use CbKST to enhance adaptation in SGs by considering not only the learner’s competence states but also teachers’ decisions based on their needs. More specifically, we have identified different needs concerning the possibility of advancing forward learning paths of SGs, as well as of reinforcing and deepening learners’ comprehension in specific subsets of competences. Therefore, we propose different recommendation strategies that allow teachers to modify the behaviour of adaptation in SGs, and we describe how we implemented and evaluated these strategies.

Formal Framework to improve the reliability of concurrent and collaborative learning games

Multi-player learning games are complex software applications resulting from a costly and complex engineering process, and involving multiple stakeholders (domain experts, teachers, game designers, programmers, testers, etc.). Moreover, they are dynamic systems that evolve over time and implement complex interactions between objects and players. Usually, once a learning game is developed, testing activities are conducted by humans who explore the possible executions of the game’s scenario to detect bugs. The complexity and the dynamic nature of multiplayer learning games enforces the complexity of testing activities. Indeed, it is impracticable to explore manually all possible executions due to their huge number. Moreover, the test cannot verify some properties on multi-player and collaborative scenarios, such as paths leading to deadlock between learners or prevent learners to meet all objectives and win the game. This type of properties should be verified at the design stage. We propose a framework enabling a formal modeling of game scenarios and an associated automatic verification of learning game’s scenario at the design stage of the development process.We use Symmetric Petri nets as a modeling language and choose to verify properties by means of model checkers. This paper discusses the possibilities offered by this framework to verify learning game’s properties before the programming stage.