IoTs for Data Collection and Trends Prediction of Online Learning Courses

Alexander Muriuki Njeru

doi:doi:10.11648/j.mcs.20200504.11

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IoTs for Data Collection and Trends Prediction of Online Learning Courses

Alexander Muriuki Njeru

Published in Mathematics and Computer Science (Volume 5, Issue 4)

Received: 29 October 2019 Accepted: 13 December 2019 Published: 14 September 2020

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Abstract

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.

Published in	Mathematics and Computer Science (Volume 5, Issue 4)
DOI	10.11648/j.mcs.20200504.11
Page(s)	72-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Internet of Things (IoT), ICT, Sensors, IoE, Big Data Mining, Data-Driven Decision Making, E-learning

References

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[2]	Chaouchi, H. (2010). The Internet of Things. ISTE Ltd and John Wiley & Sons, Inc.
[3]	Dede, C., Ho, A., & Mitros, P. (2016). Big Data. Analysis in Higher Education: Promises and Pitfalls. Retrieved from http://er.educause.edu/articles/2016/8/big-data-analysis-in-higher-education-promises-and-pitfalls.
[4]	Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G. … Andreescu, S. (2015). Health Monitoring and Management Using Internet- of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges. Proceedings-2015 IEEE International Conference on Services Computing, SCC 2015, 285–292. https://doi.org/10.1109/SCC.2015.47.
[5]	Hopgood, S. (2015). Key issues on education in Africa, pp. 16–18. Retrieved from http://www.thisisafricaonline.com/News/Key-issues-on-education-in-Africa?ct=true.
[6]	Michelle Selinger, Ana Sepulveda, J. B. (2013). Education and the Internet of Everything. Retrieved from http://www.cisco.com/c/dam/en_us/solutions/industries/docs/education/education_internet.pdf.
[7]	Mongkhonvanit, P., Dieu, V. N., & Linden, N. Van. Der. (2015). Applications of Internet of Things in E-Learning, 23 (3), 1–4. Retrieved from http://www.ijcim.th.org/past_editions/2015V23N3/23n3Page1.pdf.
[8]	Peters, J. (2016). What will be the impact of IoT on education? Retrieved from http://www.geektime.com/2016/03/07/what-will-be-the-impact-of-iot-on-education / http://www.gartner.com/newsroom/id/3165317.
[9]	Schaack, A. Van. (2009). Livescribe in K-12. Education: Research Support, (March). Retrieved from https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=7&cad=rja&uact=8&ved=0ahUKEwiw0dSDg83QAhWLurwKHREFC14QFghFMAY&url=https://www.livescribe.com/en-us/media/pdf/education/Livescribe_K-12_Research_Support.pdf&usg=AFQjCNG_Iunn8_EGDG7ckdNvBzQc6B72d.
[10]	10. Shepard, L. a. (2000). The role of classroom assessment in teaching and learning. Assessment, 95064 (310), 1–12. https://doi.org/10.1007/s11104-008-9783-1.
[11]	Taamallah, A., & Khemaja, M. (2015). Providing pervasive Learning eXperiences by Combining Internet of Things and e-Learning standards, 16, 1–12. Retrieved from http://revistas.usal.es/index.php/revistatesi/article/viewFile/eks201516498117/13774.
[12]	Vermesan, O., & Friess, P. (n. d.). Internet of Things-From Research and Innovation to. Retrieved from http://www.internet-of-things-research.eu/pdf/IoT-From%20Research%20and%20Innovation%20to%20Market%20Deployment_IERC_Cluster_eBook_978-87-93102-95-8_P.pdf.
[13]	Ardrone 2. parrot.com, “Technical specifications State of the art technology”.
[14]	J. B. Michelle Selinger, Ana Sepulveda, “Education and the Internet of Everything,” 2013.

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Alexander Muriuki Njeru. (2020). IoTs for Data Collection and Trends Prediction of Online Learning Courses. Mathematics and Computer Science, 5(4), 72-75. https://doi.org/10.11648/j.mcs.20200504.11

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Alexander Muriuki Njeru. IoTs for Data Collection and Trends Prediction of Online Learning Courses. Math. Comput. Sci. 2020, 5(4), 72-75. doi: 10.11648/j.mcs.20200504.11

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Alexander Muriuki Njeru. IoTs for Data Collection and Trends Prediction of Online Learning Courses. Math Comput Sci. 2020;5(4):72-75. doi: 10.11648/j.mcs.20200504.11

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@article{10.11648/j.mcs.20200504.11,
  author = {Alexander Muriuki Njeru},
  title = {IoTs for Data Collection and Trends Prediction of Online Learning Courses},
  journal = {Mathematics and Computer Science},
  volume = {5},
  number = {4},
  pages = {72-75},
  doi = {10.11648/j.mcs.20200504.11},
  url = {https://doi.org/10.11648/j.mcs.20200504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20200504.11},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - IoTs for Data Collection and Trends Prediction of Online Learning Courses
AU  - Alexander Muriuki Njeru
Y1  - 2020/09/14
PY  - 2020
N1  - https://doi.org/10.11648/j.mcs.20200504.11
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AB  - 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.
VL  - 5
IS  - 4
ER  -

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Author Information

Alexander Muriuki Njeru

Department of Computer Informatics, University of Szeged, Szeged, Hungary

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APA Style

Alexander Muriuki Njeru. (2020). IoTs for Data Collection and Trends Prediction of Online Learning Courses. Mathematics and Computer Science, 5(4), 72-75. https://doi.org/10.11648/j.mcs.20200504.11

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ACS Style

Alexander Muriuki Njeru. IoTs for Data Collection and Trends Prediction of Online Learning Courses. Math. Comput. Sci. 2020, 5(4), 72-75. doi: 10.11648/j.mcs.20200504.11

Copy | Download

AMA Style

Alexander Muriuki Njeru. IoTs for Data Collection and Trends Prediction of Online Learning Courses. Math Comput Sci. 2020;5(4):72-75. doi: 10.11648/j.mcs.20200504.11

Copy | Download

@article{10.11648/j.mcs.20200504.11,
  author = {Alexander Muriuki Njeru},
  title = {IoTs for Data Collection and Trends Prediction of Online Learning Courses},
  journal = {Mathematics and Computer Science},
  volume = {5},
  number = {4},
  pages = {72-75},
  doi = {10.11648/j.mcs.20200504.11},
  url = {https://doi.org/10.11648/j.mcs.20200504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20200504.11},
  abstract = {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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - IoTs for Data Collection and Trends Prediction of Online Learning Courses
AU  - Alexander Muriuki Njeru
Y1  - 2020/09/14
PY  - 2020
N1  - https://doi.org/10.11648/j.mcs.20200504.11
DO  - 10.11648/j.mcs.20200504.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 72
EP  - 75
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20200504.11
AB  - 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.
VL  - 5
IS  - 4
ER  -

Copy | Download