A Plan Recognition Process, Based on a Task Model, for Detecting Learner’s Erroneous Actions

El-Kechaï, Naïma; Després, Christophe

doi:10.1007/11774303_33

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…In learning environments ( [13], [14]), hierarchical task models were also used for designing, for instance, authoring tools, learning systems and recommender systems.…”

Section: A the Task/methods Paradigmmentioning

confidence: 99%

Using a task/method paradigm for knowledge sharing and professional training in a community of practice

Condamines

Leclet

Barramou

et al. 2012

Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON)

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This paper proposes a platform model to support teachers' professional development in a Web 2.0 approach. The goal is to enable teachers to describe their practice and exchange on it with other members of the community, allowing teachers to construct and improve their own practice by analyzing different existing practices. For the practice elicitation we use a task/method paradigm and a continuous process driven by the exchanges between users (asking for more details or particular cases). Those practices are then capitalized into individual memories. This process is based on the exchanges through the community which supposes a good matching process between members. In this way we manage a professional profile of each member favoring exchanges and knowledge dissemination (pushing the right knowledge to the right person).

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“…In learning environments ( [13], [14]), hierarchical task models were also used for designing, for instance, authoring tools, learning systems and recommender systems.…”

Section: A the Task/methods Paradigmmentioning

confidence: 99%

Using a task/method paradigm for knowledge sharing and professional training in a community of practice

Condamines

Leclet

Barramou

et al. 2012

Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON)

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show abstract

“…To recognize the task performed by the learner and the committed errors, we based on formal plan recognition techniques (Cohen et al, 1981). On an heuristics based approach, proposed in (El- Kecha and Desprs, 2006). This recognition system takes as input the actions or observable effects in the VE and the reference model.…”

Section: Task Recognition and Reference Modelmentioning

confidence: 99%

Serious Game based on Virtual Reality and Artificial Intelligence

Amokrane

Lourdeaux

Michel³

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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Virtual reality is a very interesting technology for professional training. We can mention in particular the ability to simulate the activity without real danger, the flexibility in the informations' presentation, or the exact control parameters of the simulation allows to reproduc specific situations. Today, technological maturity allows to plan increasingly a complex applications. However, in one hand, this complexity increases the difficulty, at the same time, to propose a pedagogical and narrative control (to ensure a given learning and narrative structure) and some freedom of actions (to promote the emergence of various, unique and suprised situations in order to ensure a learning-by-doing/errors). In other hand, this complexity makes difficult the tracking and understanding of learner's path. In this paper, we propose 1-a scripting model for training virtual environment combining both a pedagogical control and the emergence of pertinent learning situations and 2tracking of the learner's actions, but also analysis and automatic diagnosis tools of the learner's performances.

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“…Finally, the pattern could be associated with a method of retrospective analysis (the search for causes). The most probable cause-effect links is found using Dempster-Shafer's theory presented in [15].…”

Section: Information Concerning the Learnermentioning

confidence: 99%

PEGASE: A Generic and Adaptable Intelligent System for Virtual Reality Learning Environments

Buche¹,

Bossard²,

Querrec³

et al. 2010

IJVR

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The context of this research is the creation of human learning environments using virtual reality. We propose the integration of a generic and adaptable intelligent tutoring system (Pegase) into a virtual environment. The aim of this environment is to instruct the learner, and to assist the instructor. The proposed system is created using a multi-agent system. This system emits a set of knowledge (actions carried out by the learner, knowledge about the �eld, etc.) which Pegase uses to make informed decisions. Our study focuses on the representation of knowledge about the environment, and on the adaptable pedagogical agent providing instructive assistance

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A Plan Recognition Process, Based on a Task Model, for Detecting Learner’s Erroneous Actions

Cited by 13 publications

References 4 publications

Using a task/method paradigm for knowledge sharing and professional training in a community of practice

Using a task/method paradigm for knowledge sharing and professional training in a community of practice

Serious Game based on Virtual Reality and Artificial Intelligence

PEGASE: A Generic and Adaptable Intelligent System for Virtual Reality Learning Environments

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