KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/43203
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | V. Vanitha | en_US |
dc.contributor.author | P. Krishnan | en_US |
dc.contributor.author | R. Elakkiya | en_US |
dc.date.accessioned | 2020-12-14T16:16:20Z | - |
dc.date.available | 2020-12-14T16:16:20Z | - |
dc.date.issued | 2019-07-01 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.isbn | Not Available | - |
dc.identifier.issn | 0045-7906 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/43203 | - |
dc.description | Not Available | en_US |
dc.description.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. (C) 2019 Elsevier Ltd. All rights reserved. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | COMPUTERS & ELECTRICAL ENGINEERING; PERGAMON-ELSEVIER SCIENCE LTD; THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND; OXFORD | en_US |
dc.relation.ispartofseries | Not Available | - |
dc.subject | Genetic algorithm | en_US |
dc.subject | ACO | en_US |
dc.subject | Hybrid optimization algorithm | en_US |
dc.subject | Learning path | en_US |
dc.subject | Learning object sequence | en_US |
dc.subject | E-learning | en_US |
dc.subject | ANT COLONY SYSTEM | en_US |
dc.title | Collaborative optimization algorithm for learning path construction in E-learning | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Article | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | COMPUTERS & ELECTRICAL ENGINEERING | en_US |
dc.publication.volumeno | 77 | en_US |
dc.publication.pagenumber | 325-338 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | http://dx.doi.org/10.1016/j.compeleceng.2019.06.016 | en_US |
dc.publication.sourceUrl | PubMed id: Not Available | en_US |
dc.publication.sourceUrl | Web of Science ID: WOS:000483629600025 | en_US |
dc.publication.authorAffiliation | ICAR::National Academy of Agricultural Research and Management | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | Not Available | - |
Appears in Collections: | AEdu-NAARM-Publication |
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