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IoTs for Data Collection and Trends Prediction of Online Learning Courses
Mathematics and Computer Science
Volume 5, Issue 4, July 2020, Pages: 72-75
Received: Oct. 29, 2019; Accepted: Dec. 13, 2019; Published: Sep. 14, 2020
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Alexander Muriuki Njeru, Department of Computer Informatics, University of Szeged, Szeged, Hungary
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The rapid growth of educational data mining (EDM) is an emerging field in the academic world of research and studies focusing on collection, archiving, and analysis of data related to delivery methodology, quality of materials and student learning and assessment. The information analyzed informs the learning institution on how to improve learning experiences and how to run the institutional effectively. The people responsible for making decisions in the learning institution will able to make informed data-driven decisions. This paper explores the value of the Internet of Things (IoT) in capturing and mastering massive data for online courses to assess and identify typical learning scenarios for learners. We hope this would be a useful instrumental tool for the range of approaches in education institutions to help their struggling learners to succeed in the academic field.
Internet of Things (IoT), ICT, Sensors, IoE, Big Data Mining, Data-Driven Decision Making, E-learning
To cite this article
Alexander Muriuki Njeru, IoTs for Data Collection and Trends Prediction of Online Learning Courses, Mathematics and Computer Science. Vol. 5, No. 4, 2020, pp. 72-75. doi: 10.11648/j.mcs.20200504.11
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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