Recommending suitable learning paths according to learners’ preferences: Experimental research results.
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 lea...
| Publicado en: | Computers in Human Behavior Vol. 51; pp. 945 - 952 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Oct2015 Part B
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=108614329&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 108614329 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Oct2015 Part B vid: 51 pid: 2410 pub: Elsevier B.V. artinfo: ui: 108614329 10.1016/j.chb.2014.10.027 ppf: 945 ppct: 7 formats: tig: atl: Recommending suitable learning paths according to learners’ preferences: Experimental research results. aug: au: Kurilovas, Eugenijus Zilinskiene, Inga Dagiene, Valentina affil: Vilnius University Institute of Mathematics and Informatics, Akademijos str. 4, 08663 Vilnius, Lithuania Vilnius Gediminas Technical University, Sauletekio ave. 11, 10223 Vilnius, Lithuania su: Alternative education Artificial intelligence Computer assisted instruction Learning strategies sug: subj: Alternative education Artificial intelligence Computer assisted instruction Learning strategies keyword: Ant colony optimisation algorithm Collaborative learning Learners’ behaviour Learning paths Learning units Swarm intelligence Ant colony optimisation algorithm Collaborative learning Learners’ behaviour Learning paths Learning units Swarm intelligence ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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