CFP last date
22 July 2024
Reseach Article

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

by Delali Kwasi Dake, Davidson Kwamivi Aidam, Verite Ken Agbotse
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

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 = { },
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

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.

  1. S. Dhawan, “Online Learning: A Panacea in the Time of COVID-19 Crisis,” J. Educ. Technol. Syst., vol. 49, no. 1, pp. 5–22, 2020, doi: 10.1177/0047239520934018.
  2. D. Nambiar, “The impact of online learning during COVID-19: students’ and teachers’ perspective,” Int. J. Indian Psychol., vol. 8, no. 2, pp. 783–793, 2020, doi: 10.25215/0802.094.
  3. W. I. O’Byrne and K. E. Pytash, “Hybrid and Blended Learning: Modifying Pedagogy Across Path, Pace, Time, and Place,” J. Adolesc. Adult Lit., vol. 59, no. 2, pp. 137–140, 2015, doi: 10.1002/jaal.463.
  4. D. K. Dake, D. D. Essel, and J. E. Agbodaze, “Using Machine Learning to Predict Students’ Academic Performance During Covid-19,” no. Dm, pp. 9–15, 2021, doi: 10.1109/iccma53594.2021.00010.
  5. V. Prain et al., “Personalised learning: Lessons to be learnt,” Br. Educ. Res. J., vol. 39, no. 4, pp. 654–676, 2013, doi: 10.1080/01411926.2012.669747.
  6. J. G. Elliott, N. Hufton, L. Illushin, and F. Lauchlan, “Motivation in the Junior Years: international perspectives on children’s attitudes, expectations and behaviour and their relationship to educational achievement,” Oxford Rev. Educ., vol. 27, no. 1, pp. 37–68, 2001, doi: 10.1080/3054980020030583.
  7. D. K. Dake and B. A. Ofosu, “5G enabled technologies for smart education,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 12, pp. 201–206, 2019, doi: 10.14569/ijacsa.2019.0101228.
  8. N. Javaid, A. Sher, H. Nasir, and N. Guizani, “Intelligence in IoT-Based 5G Networks: Opportunities and Challenges,” IEEE Commun. Mag., vol. 56, no. 10, pp. 94–100, 2018, doi: 10.1109/MCOM.2018.1800036.
  9. H. Gamage, N. Rajatheva, and M. Latva-Aho, “Channel coding for enhanced mobile broadband communication in 5G systems,” EuCNC 2017 - Eur. Conf. Networks Commun., pp. 1–6, 2017, doi: 10.1109/EuCNC.2017.7980697.
  10. I. Jovović, I. Forenbacher, and M. Periša, “Massive Machine-Type Communications: An Overview and Perspectives Towards 5G,” Proc. 3rd Int. Virtual Res. Conf. Tech. Discip., vol. 3, no. Ic, pp. 32–37, 2015, doi: 10.18638/rcitd.2015.3.1.73.
  11. D. K. Dake, J. D. Gadze, and G. S. Klogo, “DDoS and Flash Event Detection in Higher Bandwidth SDN-IoT using Multiagent Reinforcement Learning,” pp. 16–20, 2021, doi: 10.1109/iccma53594.2021.00011.
  12. S. B. Baker, W. E. I. Xiang, S. Member, and I. A. N. Atkinson, “Internet of Things for Smart Healthcare.pdf,” IEEE Access, vol. 5, pp. 26521–26544, 2017.
  13. X. Li, R. Lu, X. Liang, X. Shen, J. Chen, and X. Lin, “Smart community: An internet of things application,” IEEE Commun. Mag., vol. 49, no. 11, pp. 68–75, 2011, doi: 10.1109/MCOM.2011.6069711.
  14. H. Singh and S. J. Miah, “Smart education literature: A theoretical analysis,” Educ. Inf. Technol., vol. 25, no. 4, pp. 3299–3328, 2020, doi: 10.1007/s10639-020-10116-4.
  15. Z. T. Zhu, M. H. Yu, and P. Riezebos, “A research framework of smart education,” Smart Learn. Environ., vol. 3, no. 1, 2016, doi: 10.1186/s40561-016-0026-2.
  16. D. Kwasi and A. Halil, “Artificial Intelligence Modules for Higher Educational Institutions,” Int. J. Comput. Appl., vol. 178, no. 34, pp. 17–21, 2019, doi: 10.5120/ijca2019919205.
  17. C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 40, no. 6, pp. 601–618, 2010, doi: 10.1109/TSMCC.2010.2053532.
  18. S. K. Mohamad and Z. Tasir, “Educational Data Mining: A Review,” Procedia - Soc. Behav. Sci., vol. 97, pp. 320–324, 2013, doi: 10.1016/j.sbspro.2013.10.240.
  19. S. Chang, T. Cohen, and B. Ostdiek, “What is the machine learning?,” Phys. Rev. D, vol. 97, no. 5, p. 56009, 2018, doi: 10.1103/PhysRevD.97.056009.
  20. O. F.Y, A. J.E.T, A. O, H. J. O, O. O, and A. J, “Supervised Machine Learning Algorithms: Classification and Comparison,” Int. J. Comput. Trends Technol., vol. 48, no. 3, pp. 128–138, 2017, doi: 10.14445/22312803/ijctt-v48p126.
  21. K. Hsu, S. Levine, and C. Finn, “Unsupervised learning via meta-learning,” 7th Int. Conf. Learn. Represent. ICLR 2019, 2019.
  22. D. K. Dake, J. D. Gadze, G. S. Klogo, and H. Nunoo-mensah, “Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention,” 2021.
  23. H. Shaikh, M. S. Khan, Z. A. Mahar, M. Anwar, A. Raza, and A. Shah, “A conceptual framework for determining acceptance of internet of things (IoT) in higher education institutions of Pakistan,” 2019 Int. Conf. Inf. Sci. Commun. Technol. ICISCT 2019, pp. 7–11, 2019, doi: 10.1109/CISCT.2019.8777431.
  24. B. Hirsch and J. W. P. Ng, “Education beyond the cloud: Anytime-anywhere learning in a smart campus environment,” 2011 Int. Conf. Internet Technol. Secur. Trans. ICITST 2011, no. October, pp. 718–723, 2011.
  25. C. Le Zhong, Z. Zhu, and R. G. Huang, “Study on the IOT Architecture and Access Technology,” Proc. - 2017 16th Int. Symp. Distrib. Comput. Appl. to Business, Eng. Sci. DCABES 2017, vol. 2018-Septe, pp. 113–116, 2017, doi: 10.1109/DCABES.2017.32.
  26. K. Wilson and J. H. Korn, “Attention during Lectures: Beyond Ten Minutes,” Teach. Psychol., vol. 34, no. 2, pp. 85–89, 2007, doi: 10.1080/00986280701291291.
  27. W. Xu, “Learning styles and their implications in learning and teaching,” Theory Pract. Lang. Stud., vol. 1, no. 4, pp. 413–416, 2011, doi: 10.4304/tpls.1.4.413-416.
  28. A. Dutt, “Clustering Algorithms Applied in Educational Data Mining,” Int. J. Inf. Electron. Eng., no. March, 2015, doi: 10.7763/ijiee.2015.v5.513.
  29. I. J. Sledge and J. C. Principe, “Balancing exploration and exploitation in reinforcement learning using a value of information criterion,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., pp. 2816–2820, 2017, doi: 10.1109/ICASSP.2017.7952670.
Index Terms

Computer Science
Information Sciences


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