Development of a Fuzzy Mamdani Inference System for the Assessment of the Academic Standing/Continuation Requirements in Higher Educational Institutions

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-46 Number-1
Year of Publication : 2017
Authors : Ekenta Elizabeth Odokuma, Ibidun Christiana Obagbuwa
  10.14445/22312803/IJCTT-V46P103

MLA

Ekenta Elizabeth Odokuma, Ibidun Christiana Obagbuwa "Development of a Fuzzy Mamdani Inference System for the Assessment of the Academic Standing/Continuation Requirements in Higher Educational Institutions". International Journal of Computer Trends and Technology (IJCTT) V46(1):10-14, April 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
We developed a Fuzzy Mamdani Inference System (FMIS) for the assessment of the Academic Standing and Continuation Requirements in Higher Educational Institutions with the objective of identifying early, students who are at risk of failing out. The developed model has three inputs: Cumulative Grade Point Average (CGPA), Failed Units and Repetition Status and two outputs: Academic Standing and Semester Report. Results show that the model can be used to easily identify and advice students whose performance is going below expectation, to avoid failure and dropout.

References
[1] A. Gyenesei., (2001) “A fuzzy approach for mining quantitative association rules”. Acta Cybernetica, 15: 305–320.
[2] Z. C. Johanyák (2010) Survey on Five Fuzzy Inference-Based Student Evaluation Methods; I.J. Rudas et al. (Eds.): Computational Intelligence in Engineering (SCI 313, Springer-Verlag Berlin Heidelberg) 219–228.
[3] R. S. Yadav & V. P. Singh (2012). Modeling academic performance evaluation using fuzzy c-means clustering techniques. International Journal of Computer Applications, 60(8).
[4] F. Umar, S. Suleman & E. Kwame (2017) Assessing Lecturers' Performance using Fuzzy Logic. International Journal of Computer Applications Volume 160(1) 11-14
[5] P. Cortez, & A. Silva (2008). Using data mining to predict secondary school student performance, A Proceedings of 5th Annual Future Business Technology Conference, Porto.
[6] M. J. Atkins, J. Beattie & W. B. Dockrell (1993). Assessment Issues in Higher Education, Employment Department Group: United Kingdom.
[7] A. J. Ikuomola & O. A. Arowolo (2012). Evaluation Of Student Academic Performance Using Adaptive Neuro-Fuzzy Approach. Journal of Natural Science, Engineering and Technology 11(1):11-23
[8] O. O. Oladipupo, O. J. Oyelade & D. O. Aborisade (2012). Application of Fuzzy Association Rule Mining for Analyzing Students Academic Performance. International Journal of Computer Science Issues (IJCSI), 9(6).
[9] C. Marquez-Vera, C. Romero & Ventura, S. (2011). Predicting school failure using data mining. In Educational Data Mining.
[10] J. N. Odii, T. U. Onwuama, C. L. Okpalla & A. Ejem Job Scheduling System Using Fuzzy Logic Approach. International Journal of Computer Trends and Technology– Volume 42(2)
[11] Sirigiri P., Gangadhar P.V.S.S. & Kajal K., (2012) “Evaluation of Teacher’s Performance Using Fuzzy Logic Techniques” International Journal of Computer Trends and Technology Volume 3(2) pp 200-205
[12] Upadhyay, J. & Gautam, P. (2015) Modeling of Student’s Performance Evaluation. International Journal for Innovative Research in Science and Technology 2(3): 94-98.

Keywords
Fuzzy logic, Student Performance Evaluation, Academic Standing, CGPA, Continuation Requirement.