1.1 Introduction
An artificial neural network imitates the human brain in problem solving, is a more general approach that can handle this type of problem. Hence, our attempt to build an adaptive system such as the Artificial Neural Network to predict the performance of a candidate based on the effect of these factors. 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.
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitation of the study and Definition of technical terms.
1.2 Background of Study
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.
Advising students on their class performance and motivating them in order to improve on their performance is an integral part of every instruction. The mechanisms to achieve the above aim required a technique capable of accurately predicting student achievement as early as possible and cluster them for better academic assistance. According to Lykourentzou et al, (2009), student-achievement prediction can help identify the weak learners and properly assist them to cope with their academic pursuit. Several methods and systems have been developed for the above task, most of which are artificial intelligence-based.
For instance, Lykourentzou et al., (2009) estimated the final grades of students in e-learning courses with multiple feed-forward neural networks using multiple-choice test data of students of National Technical University of Athens, Greece as input. The results obtained shows that ANN is 91.2% efficient. Junemann, Lagos, and Arriagada (2007) used neural networks to predict future student schooling performance based on students’ family, social, and wealth characteristics. The aforementioned work focused on predicting the achievement of 15-year-old secondary students on reading, mathematics and science subjects in Berlin.
In the Nigeria context, Oladokun, Adebanjo & Charles-Owaba (2008) applied multilayer perception neural network for predicting the likely performance of candidates being considered for admission into Engineering Course of the University of Ibadan using various influencing factors such as ordinary level subjects’ scores, matriculation exam scores, age on admission, parental background etc., as input variables. The results showed that ANN model is able to correctly predict the performance of more than 70% of prospective students.
However, Abass et al., (2011) applied another technique of Artificial Intelligence (AI) i.e., case-base reasoning (CBR) to predict student academic performance based on the previous datasets using 20 students in the Department of Computer Science, TASUED as the study domain. The high correlation coefficient observed between the actual graduating CGPA and the CBR predicted ones also justify the usefulness and effectiveness of AI techniques in this type of task.
In this research work, Artificial Neural Network is used to estimate students’ final grade in the university with a prediction level of 92%.
Intuitively one expects the performance of a student to be a function of some number of factors (parameters) relating to the background and intelligence of said student. It is however obvious that it will be quite difficult finding an analytical (or a mathematical) model that may acceptably model this performance/factors relationship. However one practical approach for predicting the performance of a student may be by ‘extrapolating’ from historical data of past students’ background and their associated performances.
The drawback here is the difficulty of selecting an appropriate function capable of capturing all forms of data relationships as well as automatically modifying output in case of additional information, because the performance of a candidate is influenced by a number of factors, and this influence/relationship is not likely going to be any simple known regression model.
1.3 Statement of Problem
Investigation revealed that 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.
Looking into the institution this days, you will discover that 48% of the student are actually performing very low on their academic level, whom if asked to defend his admission status cannot (i.e. sitting for the attitude test), when proper investigation is carried out, findings shows that most of them have their way into the school through bribe or the so called upper hand. Also another issue or problem for this research work is that some of the applied candidates, some are actually sound and capable of performing well when admitted, but because of some factors at the moment or surrounding the student, prevent the student from obtaining or securing his admission into the school.
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.
1.4 Aim and Objectives of the Study
The aim of the study is to design and implement a Computerized Student Academic Performance Forecasting System using artificial neural network. In achieving this aim, the following specific objectives were laid out as follows to develop a artificial neural network system software that will:
- Enhance the admission process of this institution in terms of admitting the right student into the institution.
- Create easy and friendly user interface ANNs which will allow fast operation and analysis of student output.
- Be capable of predicting student performance.
- Determine some suitable factors that affect a student’s performance.
- Model an artificial neural network that can be used to predict a candidate’s performance based on given pre requirement data given to it.
1.5 Significance of Study
This study will be of immense benefit to researchers who intend to know more on this study and can also be used by non-researchers to build more on their research work. This study contributes to knowledge and could serve as a guide for other study.
1.6 Scope of Study
The scope of the research is focused on the design and implementation of a Computerized Student Academic Performance Forecasting System using artificial neural network.
1.7 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- 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).
1.8 Definition of Terms
ANN: ANN is an acronym for Artificial Neural Network imitates the human brain in problem solving, is a more general approach that can handle this type of problem.
Database: A collection of logically related data to meet the information need of organization.
DBMS: Database management software that enable the user to define, maintain Control the database.
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