Recommending Personalized Learning Sequences for Special Needs Learner using Ant Colony Optimization

Authors

  • Jonita Roman Department of Information Technology, Sardar Vallabhbhai Institute of Technology, Vasad, Gujarat, India
  • Dr. Devarshi Mehta Gujarat Law Society, Institute of Computer Technology, Ahmedabad Gujarat, India
  • Priti Srinivas Sajja Department of Computer Science and Technology, Sardar Patel University, Gujarat, India

DOI:

https://doi.org/10.37591/joosd.v6i1.2159

Keywords:

Adaptive learning, ACO algorithm, learning technology, special needs learners, personalized learning sequences

Abstract

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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Published

2019-06-12

Issue

Vol. 6 No. 1 (2019)

Section

Research Articles

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