eprintid: 15805
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/58/05
datestamp: 2023-11-10 03:30:26
lastmod: 2023-11-10 03:30:26
status_changed: 2023-11-10 02:00:27
type: article
metadata_visibility: show
creators_name: Son, N.T.
creators_name: Jaafar, J.
creators_name: Aziz, I.A.
creators_name: Anh, B.N.
title: Meta-Heuristic Algorithms for Learning Path Recommender at MOOC
ispublished: pub
keywords: Ant colony optimization; Curricula; E-learning; Genetic algorithms; Heuristic algorithms; Knowledge based systems; Learning algorithms; Multiobjective optimization; Recommender systems, Ant Colony Optimization algorithms; E-learners; Knowledge based recommenders; Learning goals; Learning paths; Meta heuristic algorithm; Multi-objective optimization models; Online learning, Learning systems
note: cited By 25
abstract: 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.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104199949&doi=10.1109%2fACCESS.2021.3072222&partnerID=40&md5=3c3451669956ec16dc95b192d6ffde45
id_number: 10.1109/ACCESS.2021.3072222
full_text_status: none
publication: IEEE Access
volume: 9
pagerange: 59093-59107
refereed: TRUE
issn: 21693536
citation:   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.  ISSN 21693536