KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/43203
Title: | Collaborative optimization algorithm for learning path construction in E-learning |
Other Titles: | Not Available |
Authors: | V. Vanitha P. Krishnan R. Elakkiya |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR::National Academy of Agricultural Research and Management |
Published/ Complete Date: | 2019-07-01 |
Project Code: | Not Available |
Keywords: | Genetic algorithm ACO Hybrid optimization algorithm Learning path Learning object sequence E-learning ANT COLONY SYSTEM |
Publisher: | COMPUTERS & ELECTRICAL ENGINEERING; PERGAMON-ELSEVIER SCIENCE LTD; THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND; OXFORD |
Citation: | Not Available |
Series/Report no.: | Not Available |
Abstract/Description: | 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. |
Description: | Not Available |
ISBN: | Not Available |
ISSN: | 0045-7906 |
Type(s) of content: | Article |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | COMPUTERS & ELECTRICAL ENGINEERING |
NAAS Rating: | Not Available |
Volume No.: | 77 |
Page Number: | 325-338 |
Name of the Division/Regional Station: | Not Available |
Source, DOI or any other URL: | http://dx.doi.org/10.1016/j.compeleceng.2019.06.016 PubMed id: Not Available Web of Science ID: WOS:000483629600025 |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/43203 |
Appears in Collections: | AEdu-NAARM-Publication |
Files in This Item:
There are no files associated with this item.
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.