Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study

Arafat, S.; Aljohani, N.; Abbasi, R.; Hussain, A.; Lytras, M.

Abstract

In this paper we explore the interrelationship between the sociotechnical-pedagogical culture of e-learning, the emerging disciplines of Web science, Social Sensing and that of Cognitive Computation–as an emerging paradigm of computation. We comment in particular on the importance of this relation for the development of learning-analytics discourse. Moreover, we present an initial relational framework between these disciplines and suggest how these relations can be exploited to solve problems in each area. This paper argues for (a) the particular importance of the abstract class of ‘learning machines’ for Web science, (b) understanding cognitive computation as a necessary practical framework for the increasingly dominating, situated informal learning context, and (c) the potential benefit of Web science frameworks for investigating both, contemporary research questions in e-learning and the development of theories for informal ubiquitous learning. Finally, we argue that exploring links between these disciplines is necessary for improving practical research, for the purpose of developing learning-analytics methodology for evaluating the growing types of modern e-learning contexts such as the informal situated learning context.

Citation

Arafat, S., Aljohani, N., Abbasi, R., Hussain, A., & Lytras, M. (2019). Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study. Computers in Human Behavior, 92, 478-486. https://doi.org/10.1016/j.chb.2018.02.026

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2018
• Online Publication Date: Feb 22, 2018
• Publication Date: 2019-03
• Deposit Date: Sep 9, 2019
• Print ISSN: 0747-5632
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 92
• Pages: 478-486
• DOI: https://doi.org/10.1016/j.chb.2018.02.026
• Keywords: Web science, E-Learning, Social sensing, Cognitive computing, Situated-learning, Learning machines
• Public URL: http://researchrepository.napier.ac.uk/Output/1792242