Predicting Student's Performance using Data on Internet Technology Usage Behavioural Patterns

Authors

  • Esther Khakata  Faculty of Information Technology, Strathmore University, Nairobi, Kenya
  • Vincent Omwenga  Faculty of Information Technology, Strathmore University, Nairobi, Kenya
  • Simon Msanjila  Faculty of Information Technology, Strathmore University, Nairobi, Kenya

Keywords:

Internet technology, Student Performance, Stochastic Differential Equation (SDE), Predictive Model, Behavioural Patterns

Abstract

Predicting student performance is becoming increasingly important to the learners and other stakeholders. This is due to the central role that prediction plays in planning for resources in a constrained learning environment. Availability of the large educational dataset has promoted the emergence of various analytical approaches aimed at providing accurate prediction. Most of these approaches have relied on data that measure specific outcome rather than tracking the process that leads to a particular outcome. This paper proposes a Stochastic Differential Equation predictive model that evaluates in an analytic way the process leading to a certain level in student performance. The model will use data based on the behavioural patterns in the use of Internet Technology.

References

  1. Nath, K, Dhar, S, & Basishtha, S. (2014). Web 1.0 to Web 3.0—evolution of the Web and its various challenges. International Conference on Reliability, Optimization and Information Technology (ICROIT) (pp. 86–89). Faridabad: IEEE. doi:10.1109/ICROIT.2014.6798297.
  2. Kop, R. (2011). The challenges to connectivist learning on open online networks: learning experiences during a massive open online course. In The International Review of Research in Open and Distance Learning, Special Issue - Connectivism: Design and Delivery of Social Networked Learning, 12(3).
  3. Bradley, J, Barbier, J, & Handler, D. (2013). Embracing the Internet of Everything to capture your share of $ 14.4 trillion.
  4. Omwenga V. (2016). Context-Centered Mobile Application Development for Effective Adoption of Mobile Technology. Journal of Computer Engineering Vol. 18, Issue 1, 01-07.
  5. Usamah M., Norlinda B. Pauziah M., and Rosni K. (2013). An Ovreview of Using Academic Anlytics to Predict and Improve Students’ Achievement: A proposed Proactive Intelligent Intervention. IEEE 5th Conference on Engineering Education (ICEED)
  6. Spennemann, D. H.R. (2007). Learning and teaching 24/7: daily internet usage patterns at nine Australian universities. Campus-Wide Information Systems, Vol. 24 Issue: 1, pp.27-44, doi: 10.1108/10650740710726473
  7. Anunobi, C., & Ogbonna, A. (2013). Web 2.0 use by librarians in a state in Nigeria. Developing Country Studies, 2(5), 7-66. Osunade, O., & Ojo, O. (2012). Library and internet usage: a case study of the University of Ibadan. The Information Technologist, 3(2), 19-29.
  8. Hughes G. and Dobbins C. (2015). The utilization of Data Analysis Techniques in Prediction Student Performance in Massive Open Online Courses (MOOCs). Research and Practice in Technology Enhanced Learning 10:10
  9. Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 (2), 179–211
  10. Amirah M. S., Whidah H. and Nur’aini A. R. (2015). A Review on Predicting Student’s Performance using Data Mining Techniques. Procedia Computer Science 72, 414-422
  11. Anjali B. R. and Ankita A. N. (2017). Student Performance Prediction Using Decision Tree Technique. International Journal of Computational Intelligence Research 13 (7) 1735-1741
  12. Nguyen T. Lucas D., Tom´aˇs, H., Artus K., Alexandros N., and Lars S. (2011). Factorization Technique for Predicting Student Performance. Educational Recommender Systems and Technologies: Practices and Challenges. 10.4018/978-1-61350-489-5.ch006. 
  13. Anderson, A, Huttenlocher, D, Kleinberg, J, & Leskovec, J. (2014). Engaging with massive online courses. In Proceedings of the 23rd International Conference on World Wide Web (WWW ’14) (pp. 687–698). New York: ACM Press. doi:10.1145/2566486.2568042.
  14. Verhallen T. M. and Pieters R. (1984). Attitude theory and Behavioural Costs. Journal of Economic Psychology 5(3), 223-249
  15. Shugan S.M. (1980). The cost of thinking. Journal of Consumer Research 7, 99-111
  16. Ajzen I. and Fishbein M. (1977). Attitude-behaviour: A theoretical analysis and review of empirical research. Psychological Bulletin 84, 888-918.
  17. Posh O. and Wälde K. (2010). On the Non-Causal Link Between Volatility and Growth. CREATES Economics Working Paper 2009-10, University of Aarhus, School of Economics and Management
  18. Feicht R. and Stummer W. (2010). Complete Closed-form Solution to Stochastic Growth Model and Corresponding Speed of Economic Recovery. IWQW Discussion Paper Series, No. 05/2010
  19. Acemoglu D. and Guerrieri V. (2008). Capital Deepening and Non-Balanced Economic Growth. Journal of Political Economy, 116(3): 467-498
  20. Bucci A., Colapinto C., Forster M. and Torre D. L. (2008). On Human Capital and Economic Growth with Random Technology Shocks (To check further on the journal)
  21. Palacios, M. (2010). Human capital as an asset class: Implications from a general equilibrium model. Working paper, Vanderbilt University, Owen Graduate School of Management
  22. Jensen B. S. and Richter M. (2007). Stochastic One-sector and Two-sector Growth Models in Continuous Time. Stochastic Economic Dynamics, Part 5 pp 167-216. Business School Press Copenhagen
  23. Williams N. (2004). Small Noise Asymptotics for a Stochastic Growth Model. Journal of Economic Theory, 119(2): 271-298
  24. King R. G. and Robelo S. T. (1993). Transitional Dynamics and Economic Growth in the Neoclassical Model. American Economics Review, 83(4): 908-31

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

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Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

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How to Cite

[1]
Esther Khakata, Vincent Omwenga, Simon Msanjila, " Predicting Student's Performance using Data on Internet Technology Usage Behavioural Patterns " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1733-1740, January-February-2018.