eprintid: 14558
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/45/58
datestamp: 2023-11-10 03:29:08
lastmod: 2023-11-10 03:29:08
status_changed: 2023-11-10 01:57:13
type: conference_item
metadata_visibility: show
creators_name: Ngo, S.T.
creators_name: Jaafar, J.
creators_name: Van Doan, T.
creators_name: Lac, D.P.
creators_name: Bui, A.N.
title: The Effectiveness of Reference Point Selection Methods for Compromise Programming in Multi-Criteria Learning Path Search Algorithm
ispublished: pub
keywords: Curricula; Learning algorithms; Learning systems; Multiobjective optimization; Optimal systems; Recommender systems, Compromise programming; Learning paths; Massive open online course; Multi-criteria learning; Multi-objectives optimization; Optimal solutions; Path search algorithms; Reference point selection; Selection methods; Study habits, Genetic algorithms
note: cited By 3; Conference of 4th International Conference on Information Management and Management Science, IMMS 2021 ; Conference Date: 27 August 2021 Through 29 August 2021; Conference Code:175047
abstract: Massive Open Online Courses (MOOC) plays an increasingly important role in changing study habits. MOOC providers o?er thousands of courses with a wide variety of content to the learners. As a result, learners can choose an appropriate learning path based on specifc skills and knowledge. This study presents an alternative normalization method for compromise programming to build an automated learning path recommender system that we published in earlier 2021. In the previous study, we used compromise programming combined with evolutionary algorithms to approach the multi-objective optimization problem. It is effective in guiding the agents in the search process for optimal solutions. However, fnding the optimal solution that closes to the reference point without a suitable normalization and reference point selection method may affect the quality of the solution and make it difcult to assign values to the weight parameters for a particular objective. This paper describes the recommender and alternative editing to the optimization model. We perform experiments with the original data set to compare the effectiveness of the methods. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
date: 2021
publisher: Association for Computing Machinery
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120905007&doi=10.1145%2f3485190.3485236&partnerID=40&md5=de417560895af4985ab6f58502d5b254
id_number: 10.1145/3485190.3485236
full_text_status: none
publication: ACM International Conference Proceeding Series
pagerange: 296-302
refereed: TRUE
isbn: 9781450384278
citation:   Ngo, S.T. and Jaafar, J. and Van Doan, T. and Lac, D.P. and Bui, A.N.  (2021) The Effectiveness of Reference Point Selection Methods for Compromise Programming in Multi-Criteria Learning Path Search Algorithm.  In: UNSPECIFIED.