51 research outputs found

    Games in Higher Education

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    International audienceThis entry presents an overview of how and why Learning Games are used in higher education.Learning Games can be defined as games that are designed to captivate the learners’ attention and facilitate their learning process. They have explicit educational purposes and can be used for teaching at all levels of education. All types of games can be used for learning: board games, card games, role-playing games, First Person Shooter games, simulation games, management games, puzzle games, treasure hunts…The main characteristic of Learning Games for higher education is the fact that they are designed to teach specific complex skills taught at university or during professional training programs. Unfortunately, it is not infrequent to observe strong opposition on the part of this target audience to this mode of learning, that these adult students associate with children.The use of Learning Games in primary school seems natural to teachers and is encouraged by specialists in didactics and neuroscience. This learning technique is much less frequently used in middle school and is almost completely absent from higher education. Yet teachers at all these levels are faced with the same problems, such as lack of motivation and investment, for which games are known to be an effective solution. This entry presents an overview of the games that can be used for higher education and the reasons why some teachers and students still show resistance to this type of learning. The numerous advantages of games for higher education will then be presented, citing games presently used in universities, in graduate schools and for professional training. Finally, thisDraft : Marfisi-Schottman I. (2019) Games in Higher Education. In: Tatnall A. (eds) Encyclopedia of Education and Information Technologies. Springer, Chamentry presents the current research questions that need to be addressed concerning the design of games for higher education and the acceptance of these games by teachers

    Towards data exchange formats for learning experiences in manufacturing workplaces

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    Manufacturing industries are currently transforming, most notably through the introduction of advanced machinery and increasing degrees of au- tomation. This has caused a shift in skills required, calling for a skills gap to be filled. Learning technology needs to embrace this change and with this contri- bution, we propose a process model for learning by experience to understand and explain learning under these changed conditions. To put this process into practice, we propose two interchange formats for capturing, sharing, and re- enacting pervasive learning activities and for describing workplaces with in- volved things, persons, places, devices, apps, and their set-up

    Virtual Maintenance, Reality, and Systems: A Review

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    Virtual Reality is a computer-generated, mock environment that can allow people to interact with it in a seemingly real way by using certain types of specialized equipment. It is mainly used for training or educational purposes and allows for “real-life” training in a safe and monitored environment. Virtual training can be used in many different fields such as medical, military, biomedical research, aviation, and many others. However, this paper reviews the most cited publications related to the application of virtual reality for training in the United States Military. As a result, researchers can find research venues based on the challenges, risk, and infrastructures

    Collaborative Virtual Training with Physical and Communicative Autonomous Agents

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    International audienceVirtual agents are a real asset in collaborative virtual environment for training (CVET) as they can replace missing team members. Collaboration between such agents and users, however, is generally limited. We present here a whole integrated model of CVET focusing on the abstraction of the real or virtual nature of the actor to define a homogenous collaboration model. First, we define a new collaborative model of interaction. This model notably allows to abstract the real or virtual nature of a teammate. Moreover, we propose a new role exchange approach so that actors can swap their roles during training. The model also permits the use of physically based objects and characters animation to increase the realism of the world. Second, we design a new communicative agent model, which aims at improving collaboration with other actors using dialog to coordinate their actions and to share their knowledge. Finally, we evaluated the proposed model to estimate the resulting benefits for the users and we show that this is integrated in existing CVET applications

    Communicative Capabilities of Agents for the Collaboration in a Human-Agent Team

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    International audienceThe coordination is an essential ingredient for the human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. In this paper, we propose a behavioral architecture C 2 BDI that allows to enhance the knowledge sharing using natural language communication between team members. We define collaborative conversation protocols that provide proactive behavior to agents for the coordination between team members. We have applied this architecture to a real scenario in a col-laborative virtual environment for training. Our solution enables users to coordinate with other team members

    Task-Oriented Conversational Behavior of Agents for Collaboration in Human-Agent Teamwork

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    International audienceCoordination is an essential ingredient for human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. This paper proposes a be-havioral architecture C 2 BDI that enhances the knowledge sharing using natural language communication between team members. Collaborative conversation protocols and resource allocation mechanism have been defined that provide proactive behavior to agents for coordination. This architecture has been applied to a real scenario in a collaborative virtual environment for learning. The solution enables users to coordinate with other team members

    Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

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    We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for a given grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, S4Eq, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programsComment: 30 pages including appendi

    Collaborative Virtual Training with Physical and Communicative Autonomous Agents

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    International audienceVirtual agents are a real asset in collaborative virtual environment for training (CVET) as they can replace missing team members. Collaboration between such agents and users, however, is generally limited. We present here a whole integrated model of CVET focusing on the abstraction of the real or virtual nature of the actor to define a homogenous collaboration model. First, we define a new collaborative model of interaction. This model notably allows to abstract the real or virtual nature of a teammate. Moreover, we propose a new role exchange approach so that actors can swap their roles during training. The model also permits the use of physically based objects and characters animation to increase the realism of the world. Second, we design a new communicative agent model, which aims at improving collaboration with other actors using dialog to coordinate their actions and to share their knowledge. Finally, we evaluated the proposed model to estimate the resulting benefits for the users and we show that this is integrated in existing CVET applications

    Collaborative Behaviour Modelling of Virtual Agents using Communication in a Mixed Human-Agent Teamwork

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    International audience—The coordination is an essential ingredient for the mixed human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. In this paper, we proposed a collaborative conversational belief-desire-intention (C 2 BDI) behavioural agent architecture that allows to enhance the knowledge sharing using natural language communication between team members. We defined collaborative conversation protocols that provide proactive behaviour to agents for the coordination between team members. Furthermore, to endow the communication capabilities to C 2 BDI agent, we described the information state based approach for the natural language processing of the utterances. We have applied the proposed architecture to a real scenario in a collaborative virtual environment for training. Our solution enables the user to coordinate with other team members
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