1. Students’ Academic Performance Modeling and Prediction: A Fuzzy Based Approach
Etuk, S. O., Oyefolahan, I. O., Zubair, H. A, Babakano,F. J., Bima, M.E.
School of Information and Communication Technology,
Federal University of Technology, Minna, Nigeria
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

2. Introduction The importance of higher education to a Nation.
The aim of the admission process.
The worrisome decline in performance.
Teachers are considered to be most responsible observers who not only engage class but also monitor the behavior and understanding of students (Arora et al, 2013).
Hence, if an instructor can properly assess and predict student performance early enough or half way into their first session; then the instructor can take appropriate action to greatly improve the teaching-learning processes geared at improving students’ performances
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

3. Research Questions
Why do some students’ performance drop in year one despite being admitted on merit?
How can we predict their likely performance after admission?
What can be done to improve their performance?
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

4. Likely Factors Affecting Some Students’ Academic Performance
Students academic background
Parental Demography
Academic Role Model
Motivation to learn
Academic environment
Teaching styles and methods
And so on….
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

5. Methodology The fuzzy logic Figure 1: A Fuzzy logic process
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria ce

6. Fuzzy logic process..contd
Crisp Input Values:
O’level result
Motivation to learn
Parent’s literacy level
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

7. Fuzzy logic process..contd
Crisp Output Values:
S/N
Output Variable
Class
CGPA 1
Good
1st Class – 2nd Class Upper
4.5 – 5.0
3.5 – 4.49 2
Average
2nd Class Lower
2.5 – 3.49 3
“below average”
3rd Class
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

8. The Predictive Model The fuzzy based model: Where: J: Jamb score
Where:
J: Jamb score
JAveS : Average Jamb Score.
Wol: Weight of O’ Level Result (minimum 5 credit passed courses).
d.f: Dynamic factor.
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

9. Findings
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

10. Discussion and Conclusion
The result from the model when compared with the students’ 1st semester result shows 75% accuracy and absolute error less than 1.0 in each case.
A fuzzy based model was used because of its ability to account for uncertainties.
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

11. Discussion and Conclusion
From the result, it implies students performing below the predicted values need very special attention.
This handy information to the teachers as early as mid session of year one still gives the instructors time to decide on the best teaching techniques for the students. This will improve teaching and learning as well as advices to the students before the end of the session when students with below 1.5 CGPA are withdrawn.
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

12. Future Work
This research study is at the preliminary stage and on-going. An oral interview of students is recommended so as to tackle the limitation of questionnaire and this may likely be an improvement over this model
Also, this model can be extended to other departments and faculties of the University.
3rd Big Data Analytics and Innovation Conference, November, NDC, Abuja, Nigeria

13. Thank you for your attention!