Objective
NAT*LAB is a project that intends to exploit the most advanced technologies and know-how available today in Europe in order to start a cooperative, coordinated and long term effort addressing the issue of a methodology for (semi-)automatic knowledge acquisition for student modeling.
Prototypes have been developed, incorporating advanced artificial intelligence features, which address knowledge acquisition by students.
In the DELTA workplan, this issue is recognized to be of fundamental importance for the authoring and delivery processes. It is clear to the proposers that this is also one of the hardest goals to be reached in the medium-long term. Any intermediate step towards that goal requires not only a critical mass but also a critical quality of research, which can be ensured only by a few selected groups in Europe. The scope of the project covers the indications provided in the workplan, under Task 2.3 i.e. design and develop the shell of a knowledge-based system to represent the characteristics of a learner at different stages of understanding during the learning process. It is also recognized in the DELTA workplan that the set of tasks requires cognitive approaches to the learner model and should be fostered by the results of on-going evaluations on the use of interactive learning systems.
Why NAT*LAB The important technical and methodological contributions of NAT*LAB are ensured by three considerations: 1. the choice of the partners reflects one of the best combinations of specific knowledge, experience and skills on the one side and potential industrial exploitation on the other that may reasonably be assembled and coordinated in Europe today under the constraints of any single project; 2. the Artificial Intelligence and Cognitive Science technical components include the most advanced tools currently available, such as, for instance, overlay models of students versus belief representation and reasoning under multiple viewpoints; tutor-driven dialogues, guided discovery learning and mixed initiative dialogues; machine learning and the automatic construction of deviating models of the learner as a result of experimental data; 3. the methodological component addresses exactly the issue of how to convey incrementally into the learner expert system (L.E.S.) the results of on-going experimentations/evaluations on the use of interactive learning systems including a model of the student, in order to improve the adequacy and the completeness of the available student models.
What Is NAT*LAB from a technical viewpoint The technical kernel of the project consists of the design development, delivery, evaluation of incrementally more refined prototype L.E.S.s. From an initial "in vitro" prototype, by means of "inverted dialogues" (dialogues such that the L.E.S. plays the role of a student and the student "debugs" the system) one collects a growing catalogue of possible interpretations of mistakes (misconceptions) that enrich the subsequent versions of the system, when they have been included into the knowledge available to the L.E.S. Insofar as this inclusion will be automatic, we will be able to talk of a kind of "learning" by the L.E.S. as a result of its own experience with the student. The project includes specific contributions to the methodology from workpackages that will investigate issues in machine learning, second generation expert systems, dialogue management, reason maintenance in a multiple belief space. As the software / hardware technology needed for obtaining a reasonable efficiency with the before mentioned tools is currently above the one available for the delivery of courseware, we will also evaluate how concurrent machines, at present just appearing on the market, may in the medium term be adequate as implementation media with respect to reaction times and costs of the resource.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences software
- natural sciences computer and information sciences artificial intelligence expert systems
- natural sciences computer and information sciences artificial intelligence machine learning
- social sciences psychology cognitive psychology
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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Coordinator
20122 Milano
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.