Multidimensional decision model for classifying learners: the case of massive online open courses (MOOCs). Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Brigui-Chtioui, Imène; Caillou, Philippe
• Is Part Of: Journal of decision systems. Volume 26:Issue 2(2017)
• Page Start: 170
• Page End: 188
• Publication Date: 2017-04-03
• Subjects: Knowledge transfer -- MOOC -- multi-criteria decision model -- clustering -- multi-agent theory; Decision support systems -- Periodicals; Management information systems -- Periodicals; Information resources management -- Periodicals; Information storage and retrieval systems -- Periodicals; Management -- Communication systems -- Periodicals; Decision support systems; Information resources management; Information storage and retrieval systems; Management -- Communication systems; Management information systems; Periodicals; 658.40305
• DOI: 10.1080/12460125.2017.1252235
• Languages: English
• ISSNs: 1246-0125

Abstract

Abstract: A new era of learning is arising, due to the development of the digital world and the maturity of web technologies. Massive open online courses (MOOCs) have emerged in this context and are challenging classical learning in spite of boundaries of time and space. They aim to provide good-quality education to masses that cannot be part of traditional university and school learning processes. This emancipation is coupled with a large amount of data collection via learning platforms. These data constitute a great opportunity to study interactions and to profit from this, in order to optimise learning and knowledge transfer. The challenge in creating value is how to represent, analyse and reuse data in order to characterise learners and so lead to better knowledge transfer. We focus, in this article, on the context of MOOC platforms based on Open edX (The leading online courses platform, initially developed by MIT and Harvard). The purpose of this paper is to propose a decision model that takes advantage of the different data-sets available on the learning platform in order to classify learners based on their behaviour and expertise. To this end, we propose a multi-agent approach that represents human actors using intelligent agents. A coordinator agent implements the multi-criteria decision model in order to optimise the learning process. This agent employs clustering algorithms and has an overview of the learning platform that enables it to assist learners and to learn from experiences and about other agents' behaviour.