Pierre Dillenbourg (1995) The role of artificial intelligence techniques in training software CBT Forum 1/95, pp. 6-10.
Abstract This paper does not attempt to review the large number of ideas, techniques or systems developed during the last 15 years of research in 'artificial intelligence and education (AI&Ed). The reader interested in this topic can read Wenger's synthesis (1987), which is not recent but gives an excellent overview of the ideas and principles developed in AI&Ed. We focus here on a body of work which is now rather stable and constitutes the core part of AI&Ed. It that can be summarized in three points:
This paper explains these three points, especially the link between the model of expertise and the types of interactions. This link is bi-directional: the model supports some interactions, but, conversely, the targeted interactions impact on the way expertise is represented in the system.
- The major contribution of AI to educational and training software is the possibility to model expertise. This expertise is the main feature of AI-based courseware: the system is able to solve the problems that the learner has to solve. The system is knowledgeable in the domain to be taught. Of course, other computing techniques can produce a correct solution. The interest of AI techniques is less their ability to produce a correct solution than the way that this solution is constructed. For instance, some complex AI systems have been design to model the resolution of simple subtraction such as '234-98', while any computer language can produce the correct solution (Burton & Brown, 1982).
- This modelled expertise enables the system to conduct interactions that could be not conducted if the system worked with pre-stored solutions. Since artificial intelligence was originally intended to reproduce human intelligence, the techniques available for modelling expertise are to some extent human-like. Actually, the educational use of AI techniques does not require that these techniques are the prefect image of human reasoning. More modestly, it requires that AI techniques support expert-learner interactions during problem solving. Some degree of similitude may be necessary if we want the expert to talk about its expertise in a way which can be understood by the learner. For instance, neural network techniques are considered as a more detailed account of human reasoning than the symbolic techniques used in expert systems. Nevertheless, the use of neural networks in courseware raises the interaction issue: how does the system communicate with the learner about the knowledge encompassed in each of its nodes? From the courseware perspective, the quality of AI techniques is not their degree of psychological fidelity but the extent to which they support interactions which are interesting from a pedagogical viewpoint.
- The types of interactions supported by AI techniques are important for some learning objectives. These interactions are especially relevant when the goal is to acquire complex problem solving skills. Other learning objectives can be pursued with simpler interactions techniques, like multiple-choice questions. Since the development of an AI-based software is more costly that standard courseware (especially, those designed with advanced authoring tools), these techniques should be used only when they are really required.
(Almost) the same paper in french Pierre Dillenbourg and Silvere Martin-Michiellot (1995) Le role des techniques d'intelligence artificielle dans la formation. CBT Forum 1/95, pp. 6-10.
Pierre Dillenbourg (1994) Evolution épistémologique en EIAO 1 (1), pp. 39-52.
Résumé . Le concept de connaissance a fortement évolué au cours des dernières années. La connaissance n'est plus perçue comme une substance, mais comme une capacité à interagir. J'illustre cette évolution épistémologique par deux exemples: l'explication et la modélisation de l'apprenant. Initialement considérée comme la transmission d'une trace du raisonnement de l'expert, l'explication est aujourd'hui vue comme le résultat d'une construction commune. De façon similaire, le diagnostic n'est plus considéré comme une photographie neutre des connaissances du sujet, mais comme le résultat d'un processus interactif de compréhension mutuelle. AbstractThe concept of knowledge has recently evolved from a substance to a capacity to interact. I illustrate this epistemological evolution with two examples: explanation and learner modelling. Explanation is not perceived any more as transmiting the trace of the expert's reasoning, but as the result of a shared construction. Similarily, a diagnosis is not considered any more as a neutral snapshot of the learner's knowledge, but as the result of a mutual understanding process.
Pierre Dillenbourg & François Lombard (à paraître) Critique du langage-auteur "Authorware" , Revue EPI.
Résumé .Cet article présente quelques points forts et points faibles du langage-auteur Authorware Professional, produit par la société Macromedia. Il s'agit d'un langage de haut niveau spécialisé pour la conception de logiciels éducatifs, dans la lignée des langages Tutor, Pilot et autre TenCORE. Authorware est plus spécialisé que des systèmes tels que Hypercard, Visual Basic ou Toolbook qui n'ont pas été conçus spécifiquement pour développer des applications éducatives. Il se différencie aussi de produits tels que Director qui permettent de créer des présentations: ces derniers sont plus performants sur le plan des effets visuels et sonores (surtout les animations 3D), mais sont moins riches sur le plan de l'interaction. Authorware fonctionne sur Mac et sous Windows et possède une interface auteur qui est pratiquement identique sur les deux machines.Un langage-auteur doit concilier trois caractéristiques pourtant partiellement contradictoires: la facilité, la productivité et la puissance. Nous illustrons ces trois caractéristiques par différents aspects d'Authorware.
Pierre Dillenbourg & Patrick Jermann (à paraître) Le paradoxe de la machine 'sociale' INTERFACE. Aussi disponible en version HTML
Résumé Selon de noires augures, l'ordinateur allait déshumaniser les classes et appauvrir les relations sociales entre élèves. Ces sombres prédictions sont contredites par les pratiques actuelles. D'une part, on assiste à l'essor de la télématique, laquelle est intrinsèquement dédiée aux relations inter-utilisateurs. Certaines expériences utilisant la télématique dans l'enseignement ont montré que les relations médiatisées par le canal restreint du câble n'en étaient pas pour autant plus "froides" que les interactions en face à face . D'autre part, les didacticiels, autrefois conçus comme outils d'individualisation, sont aujourd'hui conçus pour stimuler les interactions entre utilisateurs. C'est ce paradoxe que nous analysons dans cet article. Mais reprenons l'histoire à ses débuts...
Pierre Dillenbourg (1996)
From mutual diagnosis to collaboration engines: Some technical aspects of distributed cognition (HTML version) Talk to be
presented at the 7th Conference on Artificial Intelligence and
Education. Washington, August 1995
(postscript version here). A transcript of this will appear in the Journal of AI in Education.
This contribution is based on the development and the evaluation of two learning environments in which the learner had to collaborate with the machine. This experience revealed that existing knowledge-based techniques are not appropriate to support 'real' collaboration, i.e. to cover a range of flexible, opportunistic and robust interactions which enable to agents to build a shared understanding of a problem. I argue for the design of collaboration engines which integrate dialogue models at the rule instantiation level. The role of dialogue models is not simply to improve the interface. The challenge is to develop models which account for the role of dialogues in problem solving and learning. Such models would reflect current theories on 'distributed cognition', one of the approaches placed under the 'situated cognition' umbrella. Most of the implications of these theories to the design of interactive learning environments (ILEs) that have been discussed so far concern the choice of methods (e.g. apprenticeship - Newman, 1989 - or project-oriented group work - Goldman et al, 1994) or software engineering (e.g. participatory design - Clancey, 1993). I address here the implications of these theories at a more technical level. I structured the argument as a discussion with myself, which is quite natural within the distributed cognition approach where interacting agents are viewed as forming a single cognitive system.
.Pierre Dillenbourg and Michael Baker (1996)
Negotiation Spaces in Human-Computer Collaborative Learning.
To appear in the Proceedings of COOP'96. (Juan-Les-Pins, France, June)
(Available in postscript )
Abstract. This paper compares the negotiation processes in different learning environments: systems where an artificial agent collaborate with the human learner, and systems where the computer supports collaboration between two human users. We argue that, in learning context, collaboration implies symmetry between agents at the design level and variable asymmetry at the interaction level. Negotiation is described as a collection of different spaces defined with seven dimensions: mode, object, symmetry, complexity, flexibility, systematicity and directness. We observed that human-human negotiation jumps between spaces, switching easily between modes of negotiation, connecting the various objects of negotiation while the 'disease' of human-computer collaborative systems was to be fixed within one negotiation space.
. Pierre Dillenbourg, David Traum & Daniel Schneider (1995)
Grounding in Multi-modal Task-Oriented Collaboration Paper accepted in the
EuroAI&Education Conference (Lisbon, Sept. 96)
This paper describes the first results of a series of experiments on multi-modal computer-supported collaborative problem solving. Pairs of subjects have to solve a murder story in a MOO environment, using also a shared whiteboard for communication. While collaboration if often described as the process of building a shared conception of the problem, our protocols show that the subjects actually create different shared spaces. These spaces are connected to each other by functional relationship: some information in space X has to be grounded in order to ground information is space Y. The reason to dissociate these spaces is that the grounding mechanisms are different, because the nature of information to be grounded is itself different. The second observation concerns the modality of grounding. We expected that subjects would use drawings to ground verbal utterances. Actually, they use three modes of interaction: (dialogue, drawing, but also action in the MOO environment) in a more symmetrical way. Grounding is often performed across different modes (e.g. an information presented in dialogue is grounded by an action in the MOO).
My private life...
- Best Performance on Marathon: 2 h 25 min 42''; on Zermatt-Verbier 13 H 02" (Patrouille des glaciers), on Pond Inlet - Igloolik (Baffin Island) : 32 days. See Course de l'escalade
- Favourite movies: Delicatessen, Brazil, Wings of Desire
- Domains of expertise: Belgian Beer (the best in the world), French Songs
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