Personalized Learning Path Problem Variations: Computational Complexity and AI Approaches
Document Type
Article
Publication Date
10-18-2024
Abstract
Excerpt: E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use Personalized Learning Paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs, student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the NP-Hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of the PLP variations, researchers risk making invalid comparisons and conclusions when they use different metaheuristics for different PLP problems.
Source Publication
IEEE Transactions on Artificial Intelligence
Recommended Citation
S. A. Mochocki, M. G. Reith, B. J. Borghetti, G. L. Peterson, J. D. Jasper and L. D. Merkle, "Personalized Learning Path Problem Variations: Computational Complexity and AI Approaches," in IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2024.3483190.
Comments
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