The Prediction of Internet Technology Utilization within Learning Environments

Authors

  • Khakata Esther Nyokabi  Faculty of Information Technology, Strathmore University, Nairobi, Kenya
  • Msanjila Simon Samuel  Faculty of Science and Technology, Mzumbe University, Morogoro, Tanzania
  • Omwenga Vincent Oteke   Faculty of Information Technology, Strathmore University, Nairobi, Kenya

DOI:

https://doi.org/10.32628/CSEIT206341

Keywords:

Internet Technology, Internet Utilization, Cobb Douglas Theorem, Predictive Models, Prediction Algorithms

Abstract

Internet technology has been revolutionary over the years especially in the educational sector. However, the utility of internet technology in the learning process of a student in a higher learning institution has not been determined over the years. This has been due to the evolution that has taken place in education. This paper aims at helping in the development of an algorithmic model that will be used for the prediction of internet technology utilization in learning. Specifically, the research will focus on modelling the Cobb- Douglas production theorem to predict the learning output of a given student considering the utility of the internet technology, the infrastructural investment made by the institution of higher learning and the effort of the student. The results of this ongoing research will eventually be of great importance in helping institutions of higher learning determine their returns after investing in internet technology. The students will also be informed on how to use the internet technology in a better way in order to get the best out of the resource.

References

  1. F. D. Davis, "Perceived usefulness, perceived ease of use and user acceptance of information technology.," MIS Quarterly, vol. 13, no. 3, pp. 319-340, 1989. 
  2. S. H. VanderLeest and J. Nyhoff, "Being fluent and faithful in a digital world," in Algorithmic Modeling, N. Zylstra, Ed., Calvin College, 2005. 
  3. A. Guazzelli, Predicting the future Part 2: Predictive modeling techniques, Zementis: IBM Inc, 2012. 
  4. K. Sveiby, The New Organizational Wealth:Managing and Measuring Knowledge-based Assets, San Francisco, CA: Berret-Koehler, 1997. 
  5. S. G. Hart and L. E. Staveland, "Development of NASA-TLX (Task Load Index):Results of empirical and theoretical research.," in Human mental workload, P. A. Hancock and N. Meshkati, Eds., Amsterdam, Elsevier Science, 1988, pp. 139-178.
  6. F. Paas, "Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive load approach," Journal of Educational Psychology, vol. 84, pp. 429-434, 1992. 
  7. "National Centre for Research Resources," 2013.
  8. [Online]. Available: http://science.education.nih.gov/supplements/nih4/techn ology/guide. [Accessed November 2013].
  9. "Russian Federation," 2012. [Online]. [Accessed November 2013].
  10. J. S. Peters and M. O. Okon, "Students’ perception of causes and effects of examination malpractice in the Nigerian educational system: The way forward for quality education.," Procedia-Social and Behavioral Sciences, vol. 114, pp. 125-129, 2014. 
  11. C. Anunobi and A. Ogbonna, "Web 2.0 use by librarians in a state in Nigeria," Developing Country Studies, vol. 2, no. 5, pp. 7-66, 2013. 
  12. O. Osunade and O. Ojo, "Library and internet usage: a case study of the University of Ibadan," The Information Technologist, vol. 3, no. 2, pp. 19-29, 2012. 
  13. "Hybrid Learning: Theoory and Practice," in 7th International conference, Shanghai, China, 2014. 
  14. BusinessDictionary, 2015. [Online]. Available: http://www.businessdictionary.com/definition/utility.ht ml. [Accessed December 2015].
  15. F. N. Parr, "An alternative to the Rayleigh curve for software development effort.," IEEE Transactions Software Engineering , SE-6, pp. 291-296, 1980. 
  16. K. Huisman, Technology Investment: A game theoretic real options approach., Tilburg: Tilburg University Press, 2000. 
  17. APA, American Psychological Association. [Online] Available at: http://www.apa.org/topics/personality/. Accessed 18th January 2017, 2017.
  18. G. Allport, Attitudes. In: C. Murchison, ed. A Handbook of Social Psychology. Worcester, MA: Clark University Press, p. 789–844, 1935.
  19. A. Bandura, Self-efficacy. Encyclopedia of human behavior, Volume 4, p. 71-81, 1994.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-06-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Khakata Esther Nyokabi, Msanjila Simon Samuel, Omwenga Vincent Oteke , " The Prediction of Internet Technology Utilization within Learning Environments" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.216-222, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT206341