ABSTRACT
This thesis evaluates the impact of Universities’ entry qualification requirements and the
performance of students, in order to decide how these factors bring patterns that are best
for admission. The proposed model uses the defined University’s entry qualification as
input variables and the students’ first year performance to determine the best admission
requirements. Genetic algorithm was used as the searching technique to determine the
hidden relationship between the input and the associated performance. Students’
admission data and their corresponding first year results were obtained from the
department of Mathematics, Ahmadu Bello University, Zaria. The results indicated that
the observed performance of students whose admission into Mathematics Department
through the University Matriculation Examinations, Post University Tertiary
Matriculation Examinations and O’levels depends more on their respective mathematics
and physics average performance in all the three examinations than their entry scores in
the individual examination. A comparative study using a statistical model show that the
result obtained from the genetic algorithm approach were in line with the result of the
statistical model. The model was implemented using java programming language,
developed in Netbean environment.
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TABLE OF CONTENTS
DECLARATION …………………………………………………………………………………………… iv
CERTIFICATION …………………………………………………………………………………………… v
DEDICATION ………………………………………………………………………………………………. vi
ACKNOWLEDGEMENT ……………………………………………………………………………….vii
ABSTRACT ………………………………………………………………………………………………….. ix
TABLE OF CONTENT……………………………………………………………………………………. x
CHAPTER ONE …………………………………………………………………………………………….. 1
GENERAL INTRODUCTION ………………………………………………………………………….. 1
1.1 Introduction ………………………………………………………………………………………………. 1
1.2 Background Information ……………………………………………………………………………… 3
1.3. Problem Definition and Motivation ………………………………………………………………. 5
1.4. Objective of the Study ……………………………………………………………………………….. 6
1.5 Research Methodology ……………………………………………………………………………….. 6
1.6 Contribution to Knowledge ………………………………………………………………………….. 7
1.8 Significant of the Study ………………………………………………………………………………. 9
1.9 Organization of the Thesis …………………………………………………………………………… 9
CHAPTER TWO ………………………………………………………………………………………….. 10
REVIEW OF LITERATURE ………………………………………………………………………….. 10
2.1 Introduction …………………………………………………………………………………………….. 10
2.2 History of Evolutionary Algorithms …………………………………………………………….. 10
2.2.1. Search Techniques ………………………………………………………………………………… 12
2.2.2 Evolution Theory and Genetic Algorithm ………………………………………………….. 15
2.3 Genetic Algorithms ………………………………………………………………………………….. 16
2.3.1 Applicability of Genetic Algorithm …………………………………………………………… 20
2.4 University Education in Nigeria ………………………………………………………………….. 21
2.5 Related Work ………………………………………………………………………………………….. 23
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2.6 Literature Gap …………………………………………………………………………………………. 31
CHAPTER THREE ……………………………………………………………………………………….. 33
MODELING THE STUDENT’S PERFORMANCE DECISION SUPPORT SYSTEM
…………………………………………………………………………………………………………………… 33
3.1. System Description ………………………………………………………………………………….. 33
3.2. Benchmarking Student’s Performance ………………………………………………………… 34
3.3 Features Extraction and Normalization of Data ……………………………………………… 35
3.3.1 Handling Discrepancy of Feature Extraction ………………………………………………. 35
3.3.2 Handling of Normalization………………………………………………………………………. 39
3.4 Genetic Algorithm ……………………………………………………………………………………. 42
3.4.1. Fitness Measurement …………………………………………………………………………….. 45
3.4.2 Selection ………………………………………………………………………………………………. 46
3.4.3 Crossover …………………………………………………………………………………………….. 46
3.4.4 Mutation ………………………………………………………………………………………………. 46
3.5 Basic Genetic Algorithm Procedure …………………………………………………………….. 47
3.5.1 Initial Population Generation …………………………………………………………………… 47
3.5.2 Fitness Evaluation………………………………………………………………………………….. 49
3.5.3. New Population ……………………………………………………………………………………. 49
3.5.4 Acceptance …………………………………………………………………………………………… 53
3.5.5 Termination Condition ……………………………………………………………………………. 54
CHAPTER FOUR …………………………………………………………………………………………. 55
IMPLEMENTATION OF THE DECISION SUPPORT SYSTEM ………………………… 55
4.1 Introduction …………………………………………………………………………………………….. 55
4.2 System Requirement …………………………………………………………………………………. 55
4.2.1 Hardware Requirement …………………………………………………………………………… 55
4.3.1 The Data Use for Evaluation ……………………………………………………………………. 55
4.3.2 Data Normalization ………………………………………………………………………………… 56
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4.3.3 Data Storage ……………………………………………………………………………………….. 56
4.4 System Implementation …………………………………………………………………………….. 56
4.4.1 Upload Unit ………………………………………………………………………………………….. 57
4.4.2 Subjects Selection Unit …………………………………………………………………………… 59
4.4.3 Selection Unit ……………………………………………………………………………………….. 60
4.4.4 Searching Unit ………………………………………………………………………………………. 61
4.5 Result and Discussion ……………………………………………………………………………….. 63
4.6. Validation ………………………………………………………………………………………………. 66
4.7 Conclusion ……………………………………………………………………………………………… 68
CHAPTER FIVE …………………………………………………………………………………………… 69
SUMMARY, CONCLUSION AND RECOMMENDATION ……………………………….. 69
5.1 Summary ………………………………………………………………………………………………… 69
5.2 Conclusion ……………………………………………………………………………………………… 69
5.3 Recommendations ……………………………………………………………………………………. 70
REFERENCES……………………………………………………………………………………………… 71
APPENDIX ………………………………………………………………………………………………….. 80
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CHAPTER ONE
NTRODUCTION
1.1 Introduction
University education in Nigeria has witnessed tremendous development since the
country’s independence in 1960 (Adeyemi, 2010). This is in recognition of the fact that
the national policy of education stipulates that university education in Nigeria shall
make optimum contribution to national development by intensifying and diversifying its
programmes for the development of high level manpower within the context of the
needs of the nation (FGN, 2004). One of the factors limiting the University in
performing its roles as it is required is the quality of students admitted into various
academic programmes (Adeyemo and Kuye, 2006). It is expected that an average
student admitted into the University should be able to face academic studies with ease
and pass his/her courses without engaging examination malpractice; because it is
assumed that such student would have had prior experience in public examination.
Students are expected to have sat for the Senior Secondary School Certificate
Examinations (SSCE) and passed the minimum requirement and presented themselves
for the Joint Admission and Matriculation Board (JAMB) Examination as a selection
test and pass at acceptable cut-off point before being offered admission into the
university (Salim, 2006). Despite these public examinations that the Nigerian
undergraduates had gone through, it has been observed that their performances in the
first two years of their undergraduate studies do not usually match that of the JAMB
which is used as the basis for their admission in the first place into the University
(Adeyemo and Kuye, 2006). Many students hardly pass all their first year courses and
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majority of those who successfully do so usually have poor grades. A great percentage
of university graduates in Nigeria fall below second class upper division and the number
of spillover students in various departments are equally high. The situation gets worse
as those who manage to graduate are not productive in the labour market because they
are unable to meet the expectations of the employers (Ajala, 2010).
In 2005, Universities recommended that further screening be conducted on candidates
who sat scored between 180 and 200 marks in the university matriculations examination
depending on the university where admission is being sought. The recommendation was
made with the hope that the post- JAMB screening exercise would restore the past glory
of tertiary education in the country and make university education accessible only to
those who want and need it (Ande, 2006). Hence, these lead to multiple admissions
selection criteria.
The issue of whether or not the scores in O’levels, UME and PUTME correlate to the
candidate’s performance in the university, especially in the first year has begun to
attract researchers’ attention. Some researchers claimed that O’levels and UME have no
correlation. This needs further investigation and evaluation in order to arrive at a
reasonable conclusion. It is known that the selection of students is a complex decision
making process, in which multiple selection criteria often needs to be considered.
However, the selection criteria used in higher education admission processes varies
widely among programmes and no consistent conclusions can be reached on the
predictive values of these criteria (Wilson, 1999). Statistical procedures, such as
discriminant analysis and regression analysis are traditionally used for predicting the
potential academic success of the applicant (Graham, 1991). In the world of information
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processing, there are lots of data with increasingly complex multi-domain problems
containing either real-world or computer-generated data, which the statistical data
processing tools may not be sufficient enough to handle, hence a more advanced
approach needs to be developed.
This thesis uses genetic algorithm to evaluate the admission requirement into various
university programmes using computer science of the department of mathematics,
Ahmadu Bello University Zaria as a case study. The system predicts the patterns that are
suitable for selection of students’ into the programme. Genetic algorithm has shown a
promising feature in the area of decision support system. The principle of survival of the
fittest in which genetic algorithm was modeled, could be of great benefit in the process
of random selection from the available data.
1.2 Background Information
Despite stringent measures and strategies employed by the Nigerian government to
ensure that educational standards are maintained at least at university level, students
who after passing through these vigorous examinations still perform far below
expectations. For instance, from the summary of Computer Science students’ data,
Mathematics department, Ahmadu Bello University, Zaira for 2009/2010 session, out of
173 students that were admitted into the programme none had CGPA above 4.5, 18 had
CGPA between 3.5 and 4.49, 35 had CGPA between 2.4 and 3.49, 10 had CGPA
between 1.5 and 2.39. At the end 97 student were recorded to have one or two carry
over and 2 were asked to withdraw (Departmental second semester summary,
2010). This implies that only 10.78% of the students actually had satisfactory results at
the end of their stay of first academic year. This also shows that 87.22% of the students
had academic challenges as undergraduate students. The high rate of poor academic
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achievement among undergraduate is not unconnected with the channel through which
they gained entry into the University. Ebiri (2010), observed that using JAMB as a
yardstick for admission of students into Nigerian universities has led to the intake of
poor caliber of candidates, characterized by high failure rate, increase in examination
malpractice, high spillovers and the production of poor quality output that are neither
self-reliant nor able to contribute effectively in the employment world.
Ironically, the process of selecting candidates for admission into tertiary institutions has
largely depended on some fixed combinations of some subjects taken by applicants in
their lower level classes. However, this technique has never been proved efficient in
admitting candidates that may perform well in the chosen courses. The fast growing of
candidates seeking for admission into tertiary institutions, there is a need to use past
data for decision support in admitting suitable candidate for a course of study.
Universities are facing the immense and quick growth of the volume of educational data
(Schönbrunn and Hilbert, 2006). Intuitively, this large amount of raw stored data
contains valuable hidden knowledge, which could be used to improve the decisionmaking
process of universities (keshavamurthy et al., 2010). An analysis of the existing
transaction data provides the information on students that will allow the definition of the
key processes that have to be adapted in order to enhance the efficiency of studying
(Mario et al., 2010). It is tedious and difficult to analyze such large voluminous data
and establishing relationship between multiple features manually. Our proposed system
delves into the problem of finding data patterns in admission datasets and provides a
technique to predict the performance of students in the first year in the University based
on the admission combination.
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1.3. Problem Definition and Motivation
Higher education systems all over the world nowadays are challenged by the new
information and communication technologies (Boufardea and Garofalakis, 2012).
Moreover, with the increase in competition among the prospective students into higher
institutions, most Universities are facing the daunting task of selecting the best students,
who have the ability and skills to pursue and succeed in their academic career in a
particular field of studies. This is because Universities are interested in increasing
performance. Performance is one of the means of measuring University’s quality and
reputation (Jusoff et al., 2008), thus higher institutions are becoming more interested in
predicting the paths of students, and identifying which students will require assistance
in order to graduate (Luan, 2004). In order to be able to achieve this objective, the
finding relationships and patterns that exist but are hidden among the vast amount of
educational data is needed. This knowledge will help in educational main processes
such as counseling, planning, registration, and evaluation in order to give suitable
recommendation of the students.
Predictions of qualities of entry result that should be used in admitting students into
respective programmes are published in Nigeria, mostly in medicine, education and
engineering and most of these are done using statistical approaches. The work of
Adewale et al (2007) and Luna (2004) show a great insight that the field of computer
science has a lot to offer in contributing to the knowledge evaluation and the
effectiveness of JAMB-UME Scores, post-UME scores and SSCE Scores.
The aim of this thesis is to determine how aggregation of UME, post-UME and SSCE
scores bring a pattern that is commonly attributed to the good performance of first year
students’ academic achievement at the university in the department of Mathematics,
Ahmadu Bello University, using the concept of Genetic Algorithm. The identification of
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these patterns can help in the selection process for admitting students into the various
departments.
1.4. Objective of the Study
The main objective of this thesis is to design a model using genetic algorithm that can
be employed in searching trends or pattern in student’s previous admission records. This
is achieved by using the aggregation of UME, Post UME, O’level scores against their
corresponding CGPA at the end of their first academic year in the University. Realizing
this objective can help in candidates’ selection criteria for admission process into the
university. This main goal can be achieved by means of the following objectives which
are:
1. To determine the means by which data collected can be translated to meaningful
ones.
2. To develop a model for searching hidden pattern among the available data set
using genetic algorithm.
3. To implement model of genetic algorithm
4. To test and validate the model using real data of students’ records.
1.5 Research Methodology
In designing the system, the objectives stated in section 1.4 can be achieved by
considering the following steps:
1. Data collected are subjected to the process of feature extraction and
normalization. The data gathering process involves the collection of raw data
about students, which include the UTME score, PUTME score and O’level
results (which are the entry requirements into the University). Feature extraction
is carried out since the data collected can be inconsistence, incomplete or noisy.
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This may be as a result of a number of factors ranging from data entry or
transmission problem, discrepancy in the naming convention, duplicated records
or incomplete data or removal of unwanted entries that are not required. All
these affect the analysis. The data used for the analysis was entered into an Excel
spreadsheet file. Each student was being identified using his/her JAMB number.
Also, normalization of data is carried out on each subject’s grade by using a
uniform data representation since each examination is being graded differently.
These datasets are stored and accessed using Mysql relational database.
2. The model of genetic algorithm of principle of survivor of the fittest is used in
searching through the formatted student academic performance. The different
operators of GA perform their own work by following the instruction for the
GA, till the best patterns are found.
3. The model is implemented using java with Mysql relational database which
provide the required functionalities in holding students’ academic data.
4. The performance of the model is then compared with the statistical approach
using SPSS software.
1.6 Contribution to Knowledge
Using decision support system for admission selection process by genetic algorithm, the
University admission requirement is validated against the performance at the end of
their first academic year, to detect hidden trends among the students’ performance. The
contributions of the study to knowledge are outlined below:
1. Government’s policy to promote higher education, learning and research will be
realized since the right candidates are selected and trained in the universities this
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will bring about the production of the right human resources who are the major
factors of production.
2. An efficient, detailed and unbiased procedure of using average performance for
admission into universities is put in place as against using single subject
performance for selection processes.
3. Selection of best students for university education will also make teaching and
learning easier as the best student is usually an individual who is focused and
disciplined. This will go a long way in making the goal of education achieved
effectively for economic growth and development in to the various sectors of the
nation.
4. Since it provides better admission opportunity for qualified candidates, better
qualified graduates will now be turned out into the job market as opposed to the
output that comes from persons who struggle through the universities because
they were never qualified to be there in the first place.
1.7 Scope of the Study
This research work is a case study of Computer Science, Mathematics Department of
Ahmadu Bello University, Zaria, Kaduna State. The research aim to cover records of
students admitted in three years academic session into 100 level through O’level, JAMB
and Post-JAMB scores respectively. This study therefore grew out of curiosity to find
out how prediction helps to identify and to improve students’ performance.
To the best of my knowledge, no study in the literature at my disposal has been carried
out to compare the academic achievement between undergraduate students admitted
through their O’level, Post-JAMB and JAMB scores in Ahmadu Bello University,
Zaria. The statement of the problem therefore seeks to identify best patterns at which the
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aggregation of the three examinations brings in the first year performance of the student.
This early prediction allows the instructor to provide appropriate advising or select
those with less risk for admission.
1.8 Significant of the Study
Precisely, the significance of this study is based on
1. Determining the extent to which scores in examinations conducted by the West
African Examination Council (WASSCE), National Examinations Council
(SSCE) and in conjunction with the Joint Admissions and Matriculation Board
(UME) and post-UTME to predict future academic achievement of students in
university degree examinations.
2. Develop structural models for predicting the academic achievement in
university degree examinations based on performance in public examinations.
1.9 Organization of the Thesis
The thesis is organized as follow: in the second chapter, review was made on the
predictive technique using Genetic Algorithm and literatures that were accomplished in
the area of University admission variables were reviewed. In the third chapter,
description was made on how to model finding patterns in admission combinations, by
first normalizing and later applying Genetic Algorithm. In the fourth chapter, a
proposed implementation of the decision support system is designed as followed from
chapter three, using java programming language and Msql relational database. The
thesis concludes in chapter five, with summary, conclusion and recommendation.
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