Home // International Journal On Advances in Intelligent Systems, volume 14, numbers 1 and 2, 2021 // View article

E-learning Personalization in Midwifery and Maritime: A Machine Learning Approach for Intelligent Recommender Systems

Authors:
Alexandros Bousdekis
Stavroula Barbounaki
Stefanos Karnavas

Keywords: learning profiles; learning styles; higher education; k-means clustering, Bayesian network; classification

Abstract:
The lockdown due to the pandemic of COVID-19 led to an unprecedented impact on education. Higher education institutions were forced to shift rapidly to distance and online learning. On the one hand, this fact revealed the weaknesses of adoption and utilization of e-learning strategies and technologies, but, on the other hand, it resulted in a digital revolution in education. However, the wide adoption of e-learning strategies and technologies and the complete transformation of the physical learning process to a virtual one pose the challenge of personalization of the learning process. This paper proposes a recommender system for supporting the professors in higher education of midwifery and maritime in understanding their students’ needs so that he/she adapts the e-learning process accordingly. To do this, it utilizes learning profile theory and it implements k-means clustering and Bayesian Networks (BN) The proposed approach was applied to a maritime educational institution.

Pages: 73 to 81

Copyright: Copyright (c) to authors, 2021. Used with permission.

Publication date: December 31, 2021

Published in: journal

ISSN: 1942-2679