Collaborative optimization algorithm for learning path construction in E-learning. (July 2019)

• Record Type: Journal Article
• Authors: Vanitha, V.; Krishnan, P.; Elakkiya, R.
• Is Part Of: Computers & electrical engineering. Volume 77(2019)
• Page Start: 325
• Page End: 338
• Publication Date: 2019-07
• Subjects: Genetic algorithm -- ACO -- Hybrid optimization algorithm -- Learning path -- Learning object sequence -- E-learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.06.016
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In e-learning, learning object sequencing is a challenging task. It is difficult to sequence learning objects manually due to their abundant availability and the numerous combinations possible. An adaptive e-learning system that offers a personalized learning path would enhance the academic performance of learners. The main challenge in providing a personalized learning path is finding the right match between individual characteristics and learning content sequences. This paper presents a collaborative optimization algorithm, combining ant colony optimization and a genetic algorithm to provide learners with a personalized learning path. The proposed algorithm utilizes the stochastic nature of ant colony optimization and exploration characteristics of the genetic algorithm to build an optimal solution. Performance of the proposed algorithm has been assessed by conducting qualitative and quantitative experiments. This study establishes that the hybrid approach provides a better solution than the traditional approach.