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Internet of Things and Online Learning: Intelligent Systems beyond Covid-19

by Delali Kwasi Dake, Davidson Kwamivi Aidam, Verite Ken Agbotse
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 47
Year of Publication: 2022
Authors: Delali Kwasi Dake, Davidson Kwamivi Aidam, Verite Ken Agbotse
10.5120/ijca2022921879

Delali Kwasi Dake, Davidson Kwamivi Aidam, Verite Ken Agbotse . Internet of Things and Online Learning: Intelligent Systems beyond Covid-19. International Journal of Computer Applications. 183, 47 ( Jan 2022), 38-42. DOI=10.5120/ijca2022921879

@article{ 10.5120/ijca2022921879,
author = { Delali Kwasi Dake, Davidson Kwamivi Aidam, Verite Ken Agbotse },
title = { Internet of Things and Online Learning: Intelligent Systems beyond Covid-19 },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2022 },
volume = { 183 },
number = { 47 },
month = { Jan },
year = { 2022 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number47/32250-2022921879/ },
doi = { 10.5120/ijca2022921879 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:14.126690+05:30
%A Delali Kwasi Dake
%A Davidson Kwamivi Aidam
%A Verite Ken Agbotse
%T Internet of Things and Online Learning: Intelligent Systems beyond Covid-19
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 47
%P 38-42
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The advancements in Internet of Things applications has seen a tremendous growth with 5G and later technologies. The industry 4.0 revolution of digital automation should not exempt education, especially with the ravaging COVID-19 pandemic. The sudden spread of the virus has necessitated a policy direction in online teaching and learning for most academic institutions. The traditional classroom, which has its positives, is minimal in the educational space since distance has become primary in COVID protocols. To wholly integrate traditional classroom merits in online learning, we propose an intelligent online learning system that discovers hidden learner behaviour, and improves personal learning using supervised, unsupervised, and Reinforcement Learning (RL) algorithms. The designed framework automates the online learning space and aids the instructor with lesson planning, delivery approaches, and learner groupings. The learner also constructs knowledge and discovers learning styles through a RL software agent that continuously interacts with the online system using exploration and exploitation mechanisms.

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Index Terms

Computer Science
Information Sciences

Keywords

Online Learning Internet of Things Covid-19 5G Networks Smart Education Smart Campus Machine Learning Big Data

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