Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach.
Highlights: [•] New approach for recommending suitable learning paths for different learners groups. [•] Dynamic learning paths selection approach based on swarm intelligence. [•] Modified ant colony optimisation algorithm for learning paths selection. [•] Simulation with a dynamic change of learnin...
| Publicado en: | Computers in Human Behavior Vol. 30; pp. 550 - 558 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Jan2014
|
| 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=92513845&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 92513845 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: Jan2014 vid: 30 pid: 2410 pub: Elsevier B.V. artinfo: ui: 92513845 10.1016/j.chb.2013.06.036 ppf: 550 ppct: 8 formats: tig: atl: Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach. 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: Computer simulation Intellect Learning Algorithms Animal behavior Insects sug: subj: Computer simulation Intellect Learning Algorithms Animal behavior Insects keyword: Ant colony optimisation algorithm ICT’s for human capital Learners’ behaviour Learning objects Learning paths Swarm intelligence Ant colony optimisation algorithm ICT’s for human capital Learners’ behaviour Learning objects Learning paths Swarm intelligence ab: Highlights: [•] New approach for recommending suitable learning paths for different learners groups. [•] Dynamic learning paths selection approach based on swarm intelligence. [•] Modified ant colony optimisation algorithm for learning paths selection. [•] Simulation with a dynamic change of learning paths to verify the method proposed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|