ACADEMIA IN INFORMATION TECHNOLOGY CONFERENCE PRESENTATION

1. AN INTELLIGENT
SYSTEM FOR
STUDENT
PLACEMENT USING
FUZZY LOGIC
Femi Temitope Johnson and Olufunke Rebecca Vincent
Department of Computer Science
Federal University of Agriculture, Abeokuta
Ogun State, Nigeria.

2. Presentation Outline
Abstract
Introduction
Educational System in Nigeria
Related Works
Methodology
Implementation and Results
Conclusion
References
2

3. Introduction
The application of Artificial Intelligence (A.I) in education is, without doubt, creating a new
dimension in the ways, manners, and approaches in which knowledge is being impacted and
passed from one person to another. An indispensable tool for individual, societal and national
development is education for without it, the development of any nation will be greatly affected.
The current and most innovative trend in 21st-century education is the application and utilization
of Artificial Intelligence through the inclusion of smart learning which is highly dependent on the
use of technology and related devices in smart education environments. With the introduction of
smart learning and other related technologies in education, researches had revealed that it is a
more efficient way through which learning can be enhanced in this present century.
3

4. Educational System in Nigeria
4
The body solely responsible for the formulation of Nigeria’s educational
system is the Federal Ministry of Education which is currently headed by
the Minister of Education (Malam Adamu Adamu). The institution was
established in the 1988 and saddled with the responsibility of using
education as a tool for fostering the development of all Nigerian citizens
to their full potentials.
The introduction of the 6-3-3-4 system of education in Nigeria was
introduced in 1973 to mirror the American system of education which
allows a student to spend 6-years for primary education, three (3) years
in junior secondary school, three (3) years of senior secondary education
and four ( 4) years tertiary education.

5. With the reception of the New National Policy on Education (2004), an increasingly huge
methodology was conveyed in 2014 through the Universal Basic Education Board (UBE)
into the instructive framework by transforming the current framework into another
framework known as the 9-3-4 framework which adjusts to the Millennium Development
Goals (MGDs) and the Education for All (EFA) approach activity.
It also accommodated some vocational skills in formal education thus enabling the
Nigerian child to become self- reliant and self-dependent. In this paper, we present a novel
approach to aid schools in making precise and accurate placement of grade nine students
into two main departments (Science and Arts) within a short period of time thereby
eliminating the challenges and problems posed by the traditional method of students
choosing a department at an earlier stage of their senior secondary education or wavering
between the departments during the period of secondary education which significantly has
a negative effect on the students overall performance and the school at large.
5
Educational System in Nigeria contd…

7. Related Works
Mangasuli Sheetal B, Savita Bakare, (2016) performed analysis on student prediction for campus placement
adopting Data Mining Algorithm and K-Nearest neighbourhood technique for different cases of students. A
total of nine hundred data sets were used, six hundred (600) data were used for training and the remaining 300
data for validating the model. The model showed an accuracy level of 92.67% and an execution time of
450(msec).
Sofowoke Deborah (2017), in her research examined the influence of social media on undergraduate student’s
performance using Neuro-fuzzy modelling technique. This technique incorporated the use of statistical tool to analyse
and test hypothesis. Result was also compared with the Neural Network model and a list of four(4) major statistical
evaluation were used for result analysis but the Neuro-fuzzy model adopted proved more significant reliable with the
minimum mean square error.
Ravi Tiwari and Awadhesh Kumar Sharma, (2015 ) presented a student placement prediction model adopting WEKA for
mining the large collected set of data. They used different algorithms to predict and classify the data with the following
result obtained: RBF network (65%), Bayes (70%), ID3 and J48 (71%), RT (73%). From the result obtained they concluded
that the RT algorithm was more accurate for the prediction.
In addition, Ravi and Jayanthi (2017), work on predicting student placements using trained data from students on a
fuzzy inference engine was able to efficiently predict and accurately analyse the numerous lists of students for placement
without breakdown. The system classified two major groups of students with five (5) input data sets and more than five
hundred (500) rules were embedded in the prediction system.

8. Related Works contd…
Bashir Khan et al., (2015) classification method for predicting and classification of secondary school students proved
effective through the application of data mining techniques to classify students marks and grades in various subjects
offered and implementation was performed using Decision Tree Algorithm.
Similarly, Mashael A. Al-Barrak and Muna Al-Razgan,( 2016) collected over 200 data of students from previous academic
performance over the years, applied a decision tree Algorithm on the collated data to predict students final Cumulative Grade
Point Average(CGPA).
B. Minaei-bidgoli et al., (2013) modelled a prediction system for student placement using test results. A support vector
machine, Artificial Neural Networks and Decision Tree Algorithm were deployed in the model resulting in an accuracy of
95%.
Dawod et al., (2017), developed a flexible fuzzy inference model to depict the pedagogical needs of students using teachers’
fine-tuned processes. The tested developed model demonstrated effectiveness in learning the interaction among students, this
aiding accurate proper placement with higher degree of accuracy.
8

9. Novel
concept
Research Methodology:
Fuzzy Logic Approach for fuzziness
rationalization at each stage
9

10. Data Requisition stage
(DSR)
⬡ Cognitive Assessment
Data (CAD)
⬡ Psychomotor and
Affective Data( PAD)
⬡ Choice Selection
Data(CSD)
Methodology
Data Analysis Stage
(DAS)
⬡ Mathematical Modelling
⬡ Fuzzy Modelling
⬡ (Linguistic. Variables
and Membership
Function)
⬡ Fuzzification and with
Knowledgebase
Representation
10
System
Implementation(SIS)
⬡ Simulation and Analysis
(MATLAB)
⬡ Program Development

11. Cognitive Assessment Data (CAD)
11
Methodology Contd…
The overall percentage in ten subjects is computed with Eq.(3),
𝑂𝐼𝑒𝑥 = 𝑠=1
10 𝐺 𝑖.𝑒𝑥
10
× 100% (3)
Placement of students in the next class is also dependent on students’ performance in external
examination assessment. Scores obtained in the same subject as reflected in Eq. (1) are graded as
shown in Eq. (4) where S.Ex denotes scores obtained in external examination and OE.ex(Grade)
represents the over-all external result grade obtainable by each student.
OE.ex(Grade) =
𝑑𝑖𝑠𝑡𝑖𝑛𝑐𝑖𝑜𝑛, 75% < 𝑠. 𝐸𝑥 ≤ 75%
𝑚𝑒𝑟𝑖𝑡, 46% < 𝑠. 𝐸𝑥 ≤ 75%
𝑓𝑎𝑖𝑙, 0% ≤ 𝑠. 𝐸𝑥 ≤ 45%
(4)

12. Psychomotor Assessment Data (CAD)
12
Methodology Contd…
These data is supplied by the student’s class teacher who over the time must have considered and noticed positive
changes in the attitudinal behaviour of the students. The PAD rating is measurable in terms of percentage. In Eq.
(5), the Psychomotor and Affective Data (PAD) is subject to the Class Teacher’s Assessment (CT.Ass.)
.
Choice Selection Data (CSD)
Parent’s Choice (PCH) is attached an appreciable significant value than the Students Choice (SCH) except
when the parent is indifferent to the choice of department. Both choices are depicted mathematically in Eq.
(6)
.

13. Fuzzy Linguistic Variable Modelling
13
Methodology Contd…
Every expert system requires input for which processing must be performed. The input specified for the
systems have related membership functions as there exist a significant interaction among the variables.
Choice Selection Data (CSD)
Since CAD comprises of two distinct variables as shown in Eq.(7), distinct ratings and membership functions
exist as shown in equations (8 and 9) respectively. However, the other variables are assigned respective
membership functions as shown in Eq. (10) and Eq.(11).
.

14. 14
Fuzzification and Knowledge Representation
14
Methodology Contd…
Each fuzzy variable is turned into linguistic type with assigned membership function. An inference engine
interacts with the linguistic variables and the knowledgebase by mapping it to its associated rules, thus
generating a fuzzy output which can be defuzzify for a better understanding by the user. Eq. (12)
represents the triangular membership function used for defuzzifying the output where S, A and R denotes
result to indicate if a student should be placed in either Science department, Art department, or repeats the
present class.
In Eq.(13), 𝜗2
1 , 𝜗2
2 , 𝜗2
3 … 𝜗2
𝑛 are values of the different rules with the same conclusion in the
knowledgebase. The output is derived by computing the aggregate of the value obtained in equation (13)
and defuzzifying using the centroid model

15. Fuzzy rule knowledge base
15
S/N CAD PAD CSD OUTPUT
I.Ex E.Ex S.Ch P.Ch DEPT
1. P D S S S SCIENCE
2 P D A A A ARTS
3. P M A I I ART
4. F F A I S REPEAT
5. F M S I S ART
6 P M A A I ART
7 P M S S A SCIENCE
8 P M S A S SCIENCE
9 P F A I A REPEAT
10 F F S S S REPEAT
Sample fuzzy rules for departmental Placement.

16. 16
Intelligent Student Placement System Algorithm
16
INPUT: collectedData
OUTPUT: Student Department
BEGIN
1. while collectedData is not empty Do
2. for each rule in Rx of rulebase
3. compare collectedData with Rx
4. If collectedData = Rx
5. Place student in Dept.
6. Else
7. Update R
8. Endif
9. Endfor
10. Endwhile
11. For each Predicted Rx do
12. Case (switch)
13. If (switch== a)
14. StudentDept is Science
15. If (switch== b)
16. StudentDept is Art
17. If (switch== c)
18. StudentDept is Undetermined (Repeat)
19. End Case
20. End for
21. End

17. IMPLEMENTATION
& RESULT
ANALYSIS
This fuzzy idea is simply
conveyed with these
implementation images making it
possible to absorb large amounts of
data quickly.
17

18. 18
IMPLEMENTATION
The list of subjects were limited to ten (10) as some subjects were merged and embedded as one. Figure 4
shows the list of subjects and maximum allot-able score that can be obtained in the examination Parent's
Choice (PCH) is attached an appreciable significant value than the Students Choice (SCH) except when the
parent is indifferent to the choice of department. Both choices are depicted mathematically in Eq. (6)

19. 19
Implementation Contd…
Psychomotor Assessment and Internal Examination Fuzzification model

20. 20
Implementation Contd…
Surface Viewer Interface for simulations

21. 2121
Implementation Contd…
Surface Viewer Interface for simulations

22. 222222
Implementation Contd…
Analysed result of sampled parameters
Graphical Correlation between Cognitive Assessment and Placement result

23. 23232323
Implementation Contd…
In addition, technological acceptance of the system is also worth commending as it reduces the challenges faced by the
school registrar in placement of students into departments. The developed system is an interactive one with user friendly
interfaces depicted in figs. 12a and b respectively
Accuracy Evaluation and Comparison metric
0
10
20
30
40
50
60
70
80
90
100
KNN RBF NET. BAYES J48 RT SVM& ANN FUZZY
LOGIC
92.67
65
70 71 73
95 95.87Accuracy(%)
Algorithm

24. 24242424
Implementation Contd…
Graphical User Interface of the Proposed System

25. CONCLUSION.
This paper has adopted the use of Fuzzy logic to predict the most suited department for students. The
prediction criteria adopted is a novel type combining Student choice, Parent choice Class teacher’s
Assessment and school factors.
⬡ The algorithm describes the processes performed by the modelling engine in the selection and
prediction of student department. Furthermore, the framework and architecture of the system
presented depicts the variables deployed in the robust system. Output generated with an
accuracy level of 95.87% by the system is a true replica of the action the expert (school registrar)
would perform in order to place student into various department.
⬡ Conclusively, the data set for testing was limited but can be extended to an appreciable large
number depending on the available number of students to be placed into departments. However,
the fuzzy intelligent system for students’ departmental placement has been noted to enhance and
efficiently solve the rigorously task consuming mental power and enormous time of registrar
during student departmental placement. Significantly noticeable in this research is the
relationships which exist between the adopted variables and the computational time required for
the system to generate the output for each student input.
25

26. REFERENCES
⬡ B. Minaei-bidgoli, Ben Henson D.A. Kashy, G. Kortemeyer and W.F. Punch,(2003),“Predicting Student
Performance: An Application of Data Mining Methods with An Educational Web-Based System”,
Proceedings of 33rd Annual Frontiers in Education, pp. 1-6,
⬡ Bashir Khan, Malik Sikandar Hayat and Muhammad Daud Khattak,(2015) “Final Grade Prediction of
Secondary School Student using Decision Tree”, International Journal of Computer Applications, Vol.
115, No. 21, pp. 32-36
⬡ Carmona, C., Castillo, G.,Millan, E., (2007)“Discovering Student Preferences in E-Learning”, Proceedings
of the International Workshop on Applying Data Mining in e-Learning, ADML-07, pp. 33-43.
⬡ Chen, C., Hong, C., Chen, S., Liu, C., (2006), “Mining Formative Evaluation Rules Using Web-based
Learning Portfolios for Web-based Learning Systems”, Educational Technology & Society, Vol. 9, No. 3,
pp. 69-87.
⬡ Essays, UK. (November 2018), “Internal and External Factors of Effective Learning Education” Essay.
Retrieved from https://www.ukessays.com/essays/education/internal-and-external-factors-of-effective-
learning-education-essay.php?vref=1
⬡ Esposito, F., Licchelli, O., Semeraro, G., (2004), “Discovering Student Models in e-learning Systems”,
Journal of Universal Computer Science, vol. 10, no. 1, p. 47-57.
⬡ Hatzilygeroudis, I., Prentzas, J., (2004), “Using a hybrid rule- based approach in developing an intelligent
tutoring system with knowledge acquisition and update capabilities”, Expert Systems with Applications,
Vol. 26, pp. 477–492 26

27. REFERENCES
⬡ Chong, C., Chen, S., Liu, C., (2006), “Mining Formative Evaluation Rules Using Web-based Learning
Portfolios for Web-based Learning Systems”, Educational Technology & Society, Vol. 9, No. 3, pp. 69-87.
⬡ Essays, UK. (November 2018), “Internal and External Factors of Effective Learning Education” Essay.
Retrieved from https://www.ukessays.com/essays/education/internal-and-external-factors-of-effective-
learning-education-essay.php?vref=1
⬡ Esposito, F., Licchelli, O., Semeraro, G., (2004), “Discovering Student Models in e-learning Systems”,
Journal of Universal Computer Science, vol. 10, no. 1, p. 47-57.
⬡ Hatzilygeroudis, I., Prentzas, J., (2004), “Using a hybrid rule- based approach in developing an intelligent
tutoring system with knowledge acquisition and update capabilities”, Expert Systems with Applications,
Vol. 26, pp. 477–492
⬡ Indriana Hidayah, Adhistya Erna Permanasari and Ning Ratwastuti,(2013),“Student Classification for
Academic Performance Prediction using Neuro Fuzzy in a Conventional Classroom”,Proceedings of IEEE
Conference Information Technology and Electrical Engineering, pp.1- 5.
⬡ Jerry M. Mendel,(1995),“Fuzzy logic systems for engineering: A Tutorial”, Proceedings of the IEEE,
83(3):345-377
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