research-article

Recommending suitable learning paths according to learners' preferences

Authors:

Eugenijus Kurilovas,

Inga Zilinskiene,

Valentina DagieneAuthors Info & Claims

Computers in Human Behavior, Volume 51, Issue PB

Pages 945 - 951

https://doi.org/10.1016/j.chb.2014.10.027

Published: 01 October 2015 Publication History

Abstract

Recommending suitable learning paths according to learners' preferences.Experimental research results.Learners behaviour and learning styles. 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.

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Cited By

Yingchuan HChangsheng WXinyuan C(2023)Research on smart classroom learning paths and effect testing and evaluation technology based on machine learning algorithmProceedings of the 2023 6th International Conference on E-Business, Information Management and Computer Science10.1145/3644479.3644510(191-195)Online publication date: 30-Dec-2023
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Zheng YXu YWang DChen SSun MLi YGao F(2023)A Novel Two-Stage Personalized Learning Path Recommendation Approach for E-learningProceedings of the 15th International Conference on Education Technology and Computers10.1145/3629296.3629304(47-52)Online publication date: 26-Sep-2023
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Published In

cover image Computers in Human Behavior

Computers in Human Behavior Volume 51, Issue PB

October 2015

847 pages

ISSN:0747-5632

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2015

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Cited By

Yingchuan HChangsheng WXinyuan C(2023)Research on smart classroom learning paths and effect testing and evaluation technology based on machine learning algorithmProceedings of the 2023 6th International Conference on E-Business, Information Management and Computer Science10.1145/3644479.3644510(191-195)Online publication date: 30-Dec-2023
https://dl.acm.org/doi/10.1145/3644479.3644510
Zheng YXu YWang DChen SSun MLi YGao F(2023)A Novel Two-Stage Personalized Learning Path Recommendation Approach for E-learningProceedings of the 15th International Conference on Education Technology and Computers10.1145/3629296.3629304(47-52)Online publication date: 26-Sep-2023
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Cheong N(2022)Exploring Recommending Algorithms Applied in e-Learning EnvironmentsProceedings of the 8th International Conference on Industrial and Business Engineering10.1145/3568834.3568869(526-530)Online publication date: 27-Sep-2022
https://dl.acm.org/doi/10.1145/3568834.3568869
Cheong N(2022)Review and Analysis of Intelligent Recommendation System Using Machine Learning ApproachesProceedings of the 6th International Conference on Digital Technology in Education10.1145/3568739.3568756(88-92)Online publication date: 16-Sep-2022
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