International Journal of
Science and Technology Education Research

  • Abbreviation: Int. J. Sci. Technol. Educ. Res.
  • Language: English
  • ISSN: 2141-6559
  • DOI: 10.5897/IJSTER
  • Start Year: 2010
  • Published Articles: 74

Full Length Research Paper

Predictive modelling and analysis of academic performance of secondary school students: Artificial Neural Network approach

Amoo M. Adewale
  • Amoo M. Adewale
  • Department of Computer and Information Sciences, Tai Solarin University of Education, Ogun State Nigeria.
  • Google Scholar
Alaba O. Bamidele
  • Alaba O. Bamidele
  • Department of Computer and Information Sciences, Tai Solarin University of Education, Ogun State Nigeria.
  • Google Scholar
Usman O. Lateef
  • Usman O. Lateef
  • Department of Computer and Information Sciences, Tai Solarin University of Education, Ogun State Nigeria.
  • Google Scholar


  •  Received: 07 June 2017
  •  Accepted: 01 July 2017
  •  Published: 31 May 2018

References

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Adeleke RA, Ruzaini AA, Hongwu Q (2013), Risk Status Prediction and Modelling of Students' Academic Achievement-A Fuzzy Logic Approach. Research Inventory: Int. J. Eng. Sci. 3(11):07-14.

 

Ayan MNR, Gracia MTC (2013), Prediction of University Students' Academic Achievement by Linear and Logistic Models. Span J. Psychol. 11(1):275-288.
Crossref

 

Haykin S (1999), Neural Networks: A Comprehensive Foundation (2nd Edition). Macmillan College Publishing Company, New York.

 

Kalejaye BA, Folorunso O, Usman OL (2015), Predicting Students' Grade Scores using Training Functions of Artificial Neural Network. J. Natural Sci. Eng. Technol. 14(1):25-42.

 

Lykourentzou I, Giannoukos I, Mpardis G, Nikolopoulos V, Loumos V (2009), Early and Dynamic Student Achievement Prediction in E-Learning Courses Using Neural Networks. J. Am. Soc. Infor. Sci. Technol. 60(2):372-380.
Crossref

 

McCulloch WS, Pitts WA (1943), A logical Calculus of Ideas Imminent in Nervous Activity. Bull. Math. Biophys. 5:115-133.
Crossref

 

Nghe NT, Janecek KP, Haddawy P (2007), A comparative Analysis of Techniques for Predicting Academic Performance. In, Proceedings of 37th ASEE/IEEE Frontiers in Education Conference, pp. 659-669.

 

Oladokun VO, Adebanjo AT, Charles-Owaba OE (2008). Predicting Students Academic Performance using Artificial Neural Network: A case study of an Engineering Course. Pacific J. Sci. Technol. 9(1):72-79.

 

Rajasekaran S, Vijayalakshmi Pai GA (2012), Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications. PHI Learning Private Limited, New Delhi-110001, ISBN: 978-81-203-2186-1.

 

Usman OL, Adenubi AO (2013), Artificial Neural Network (ANN) model for Predicting Students' Academic Performance. J. Sci. Infor. Technol. (JOSIT) 13(2):61-71.

 

Usman OL, Alaba OB (2014), Predicting Electricity Consumption Using Radial Basis Function (RBF) Network. Int. J. Comput. Sci. Artificial Intell. 4(2):54-62.
Crossref

 

Wang T, Mitrovic A (2002). Using Neural Networks to Predict Students' Performance. Paper presented at the International Conference on Computers in Education, Auckland, New Zealand.