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:
1. To
examine the use of Artificial
Neural Network in predicting students academic performance.
2. To examine the mode of operation of Artificial
Neural Network.
3. 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.