Meta-Heuristic Algorithms for Learning Path Recommender at MOOC

Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance...

Full description

Saved in:
Bibliographic Details
Main Authors: Son, N.T., Jaafar, J., Aziz, I.A., Anh, B.N.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104199949&doi=10.1109%2fACCESS.2021.3072222&partnerID=40&md5=3c3451669956ec16dc95b192d6ffde45
http://eprints.utp.edu.my/23764/
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Petronas
id my.utp.eprints.23764
record_format eprints
spelling my.utp.eprints.237642021-08-19T13:10:38Z Meta-Heuristic Algorithms for Learning Path Recommender at MOOC Son, N.T. Jaafar, J. Aziz, I.A. Anh, B.N. Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera's courses and Vietnam work's jobs. The test results show the effectiveness of the proposed method. © 2013 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104199949&doi=10.1109%2fACCESS.2021.3072222&partnerID=40&md5=3c3451669956ec16dc95b192d6ffde45 Son, N.T. and Jaafar, J. and Aziz, I.A. and Anh, B.N. (2021) Meta-Heuristic Algorithms for Learning Path Recommender at MOOC. IEEE Access, 9 . pp. 59093-59107. http://eprints.utp.edu.my/23764/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera's courses and Vietnam work's jobs. The test results show the effectiveness of the proposed method. © 2013 IEEE.
format Article
author Son, N.T.
Jaafar, J.
Aziz, I.A.
Anh, B.N.
spellingShingle Son, N.T.
Jaafar, J.
Aziz, I.A.
Anh, B.N.
Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
author_facet Son, N.T.
Jaafar, J.
Aziz, I.A.
Anh, B.N.
author_sort Son, N.T.
title Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
title_short Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
title_full Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
title_fullStr Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
title_full_unstemmed Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
title_sort meta-heuristic algorithms for learning path recommender at mooc
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104199949&doi=10.1109%2fACCESS.2021.3072222&partnerID=40&md5=3c3451669956ec16dc95b192d6ffde45
http://eprints.utp.edu.my/23764/
_version_ 1738656518340870144