Planning and scheduling in an e-learning environment. A constraint-programming-based approach
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| Title: | Planning and scheduling in an e-learning environment. A constraint-programming-based approach |
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| Authors: | Garrido, Antonio agarridot@dsic.upv.es, Onaindia, Eva1 onaindia@dsic.upv.es, Sapena, Oscar1 osapena@dsic.upv.es |
| Source: | Engineering Applications of Artificial Intelligence. Aug2008, Vol. 21 Issue 5, p733-743. 11p. |
| Subjects: | Computer assisted instruction, Multimedia systems, Internet in education, Online information services |
| Abstract: | Abstract: AI planning techniques offer very appealing possibilities for their application to e-learning environments. After all, dealing with course designs, learning routes and tasks keeps a strong resemblance with a planning process and its main components aimed at finding which tasks must be done and when. This paper focuses on planning learning routes under a very expressive constraint programming approach for planning. After presenting the general planning formulation based on constraint programming, we adapt it to an e-learning setting. This requires to model learners profiles, learning concepts, how tasks attain concepts at different competence levels, synchronisation constraints for working-group tasks, capacity resource constraints, multi-criteria optimisation, breaking symmetry problems and designing particular heuristics. Finally, we also present a simple example (modelled by means of an authoring tool that we are currently implementing) which shows the applicability of this model, the use of different optimisation metrics, heuristics and how the resulting learning routes can be easily generated. [Copyright &y& Elsevier] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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