PREDICTING STUDENTS ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND TO THE STUDY
Predicting student academic performance has long been an important
research topic. Among the issues of education system, questions
concerning admissions into academic institutions (secondary and
tertiary level) remain important (Ting, 2008). The main objective of the
admission system is to determine the candidates who would likely
perform well after being accepted into the school. The quality of
admitted students has a great influence on the level of academic
performance, research and training within the institution. The failure
to perform an accurate admission decision may result in an unsuitable
student being admitted to the program. Hence, admission officers want
to know more about the academic potential of each student. Accurate
predictions help admission officers to distinguish between suitable and
unsuitable candidates for an academic program, and identify candidates
who would likely do well in the school (Ayan and Garcia, 2013). The
results obtained from the prediction of academic performance may be used
for classifying students, which enables educational managers to offer
them additional support, such as customized assistance and tutoring
resources.
The results of this prediction can also be used
by instructors to specify the most suitable teaching actions for each
group of students, and provide them with further assistance tailored to
their needs. In addition, the prediction results may help students
develop a good understanding of how well or how poorly they would
perform, and then develop a suitable learning strategy. Accurate
prediction of student achievement is one way to enhance the quality of
education and provide better educational services (Romero and Ventura,
2007). Different approaches have been applied to predicting student
academic performance, including traditional mathematical models and
modern data mining techniques. In these approaches, a set of
mathematical formulas was used to describe the quantitative
relationships between outputs and inputs (i.e., predictor
variables). The prediction is accurate if the error between the
predicted and actual values is within a small range.
In machine learning and cognitive science,
artificial neural networks (ANNs) are a family of statistical learning
models inspired by biological neural networks (the central nervous
systems of animals, in particular the brain) and are used to estimate
or approximate functions that can depend on a large number of inputs
and are generally unknown. Artificial neural networks are generally
presented as systems of interconnected "neurons" which exchange
messages between each other. The connections have numeric weights that
can be tuned based on experience, making neural nets adaptive to inputs
and capable of learning. For example, a neural network for handwriting
recognition is defined by a set of input neurons which may be
activated by the pixels of an input image. After being weighted and
transformed by a function (determined by the network's designer), the
activations of these neurons are then passed on to other neurons. This
process is repeated until finally, an output neuron is activated. This
determines which character was read.
The artificial neural network (ANN), a soft
computing technique, has been successfully applied in different fields
of science, such as pattern recognition, fault diagnosis, forecasting
and prediction. However, as far as we are aware, not much research on
predicting student academic performance takes advantage of artificial
neural network. Kanakana and Olanrewaju (2001) utilized a multilayer
perception neural network to predict student performance. They used the
average point scores of grade 12 students as inputs and the first year
college results as output. The research showed that an artificial
neural network based model is able to predict student performance in
the first semester with high accuracy. A multiple feed-forward neural
network was proposed to predict the students’ final achievement and to
classify them into two groups. In their work, a student achievement
prediction method was applied to a 10-week course. The results showed
that accurate prediction is possible at an early stage, and more
specifically at the third week of the 10-week course.
1.2 STATEMENT OF THE PROBLEM
The observed poor academic performance of some Nigerian students
(tertiary and secondary) in recent times has been partly traced to
inadequacies of the National University Admission Examination System.
It has become obvious that the present process is not adequate for
selecting potentially good students. Hence there is the need to improve
on the sophistication of the entire system in order to preserve the
high integrity and quality. It should be noted that this feeling of
uneasiness of stakeholders about the traditional admission system,
which is not peculiar to Nigeria, has been an age long and global
problem. Kenneth Mellamby (1956) observed that universities worldwide
are not really satisfied by the methods used for selecting
undergraduates. While admission processes in many developed countries
has benefited from, and has been enhanced by, various advances in
information science and technology, the Nigerian system has yet to take
full advantage of these new tools and technology. Hence this study
takes an scientific approach to tackling the problem of admissions by
seeking ways to make the process more effective and efficient.
Specifically the study seeks to explore the possibility of using an
Artificial Neural Network model to predict the performance of a student
before admitting the student.
1.3 OBJECTIVES OF THE STUDY
The following are the objectives of this study:
- To examine the use of Artificial Neural Network in predicting students academic performance.
- To examine the mode of operation of Artificial Neural Network.
- To identify other approaches of predicting students academic performance.
1.4 SIGNIFICANCE OF THE STUDY
This study will educate on the design and implementation of
Artificial Neural Network. It will also educate on how Artificial
Neural Network can be used in predicting students academic performance.
This research will also serve as a resource base to other scholars
and researchers interested in carrying out further research in this
field subsequently, if applied will go to an extent to provide new
explanation to the topic
1.6 SCOPE/LIMITATIONS OF THE STUDY
This study will cover the mode of operation of Artificial Neural
Network and how it can be used to predict student academic performance.
LIMITATION OF STUDY
Financial constraint- Insufficient fund tends to
impede the efficiency of the researcher in sourcing for the relevant
materials, literature or information and in the process of data
collection (internet, questionnaire and interview).
Time constraint- The researcher will
simultaneously engage in this study with other academic work. This
consequently will cut down on the time devoted for the research work.
REFERENCES
Ayan, M.N.R.; Garcia, M.T.C. 2013. Prediction of
university students’ academic achievement by linear and logistic models.
Span. J. Psychol. 11, 275–288.
Kanakana, G.M.; Olanrewaju, A.O. 2001. Predicting
student performance in engineering education using an artificial
neural network at Tshwane university of technology. In Proceedings of
the International Conference on Industrial Engineering, Systems
Engineering and Engineering Management for Sustainable Global
Development, Stellenbosch, South Africa, 21–23 September 2011; pp. 1–7.
Romero, C.; Ventura, S. 2007, Educational Data mining: A survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146.
Ting, S.R. 2008, Predicting academic success of first-year engineering students from standardized test scores and psychosocial variables. Int. J. Eng. Educ., 17, 75–80.