Recommending suitable learning paths according to learners' preferences

Experimental research results

Eugenijus Kurilovas, Inga Zilinskiene, Valentina Dagiene

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

The paper deals with the problem of personalising learning units with the main focus on finding personalised learning paths in learning units. Finding suitable learning paths is based on students' needs in terms of their learning styles. It has been shown that learning path in static and dynamic learning units can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model, mainly by adapting ant colony optimisation method based on collaboration and pheromones. In the paper, experimental results of applying the proposed approach in practise are presented. The results of empirical experiment have shown that learning in the proposed prototype of e-learning system applying created recommending method improves students' learning results and saves their learning time. This fact indicates that the developed adaptive method for personalising learning units is practically applicable in e-learning and enhances the learning quality.

Original languageEnglish
JournalComputers in Human Behavior
DOIs
Publication statusAccepted/In press - 2014
Externally publishedYes

Fingerprint

Learning
Students
Ant colony optimization
Research
Artificial intelligence
Learning systems
Experiments
Experimental Research
Ants
Artificial Intelligence
Pheromones
Swarm intelligence
Intelligence

Keywords

  • Ant colony optimisation algorithm
  • Collaborative learning
  • Learners' behaviour
  • Learning paths
  • Learning units
  • Swarm intelligence

ASJC Scopus subject areas

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

Cite this

Recommending suitable learning paths according to learners' preferences : Experimental research results. / Kurilovas, Eugenijus; Zilinskiene, Inga; Dagiene, Valentina.

In: Computers in Human Behavior, 2014.

Research output: Contribution to journalArticle

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