Recommending suitable learning scenarios according to learners' preferences: An improved swarm based approach

Eugenijus Kurilovas, Inga Zilinskiene, Valentina Dagiene

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners' preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors - by helping them to monitor, refine, and improve e-learning modules and courses according to the learners' behaviour.

Original languageEnglish
Pages (from-to)550-557
Number of pages8
JournalComputers in Human Behavior
Volume30
DOIs
Publication statusPublished - Jan 2014
Externally publishedYes

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Keywords

  • Ant colony optimisation algorithm
  • ICT's for human capital
  • Learners' behaviour
  • Learning objects
  • Learning paths
  • Swarm intelligence

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Psychology(all)
  • Arts and Humanities (miscellaneous)

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