Recommending Personalized Learning Sequences for Special Needs Learner using Ant Colony Optimization
Learning through technology is a knowledge management concept where the learning resources have to be presented in a clear and comprehensive manner to the learners. This study presents a new approach for recommending suitable learning paths for special needs learners by applying artificial intelligence technique, ‘Ant colony optimization algorithm’. The study is carried out for Attention Deficit and Hyperactive Disorder (ADHD) and children facing Learning Disability (LD). We propose a probabilistic approach for the heuristic search of learning objects in creating personalized learning sequences. Learning paths are recommended to the learners, using learner’s preference and personal traits. As the learner takes up learning contents, depending on the learner’s performance on the fly, new learning sequences are generated and provided to the learners.
Cite this Article
Jonita Roman, Devarshi Mehta, Priti Srinivas Sajja. Recommending Personalized Learning Sequences for Special Needs Learner using Ant Colony Optimization. Journal of Open Source Developments. 2019; 6(1): 32–39p.
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