Computer Science

Predicting Student Performance Using Artificial Neural Network

Predicting Student Performance Using Artificial Neural Network


I am a student of the above institution in the computer science department who currently seeks to carry out research work on “design and implementation of a computer-based seaport billing system”, I took this research upon me after my research survey and finding for problems that need attention and solution.

The observed poor quality of graduates of students of this institution in recent times has been partly traced to inadequacies of some or most of the lecturers in this University. In this study, an Artificial Neural Network (ANN) model, for predicting the likely performance of students will be developed and tested.

I will also identify the various factors that may likely influence the performance of students. An implementation of a user-friendly software tool for predicting the students’ performance is based on a neural network classifier. This tool has a simple interface and can be used by an educator for classifying students and distinguishing students with low achievements or weak students who are likely to have low achievements. The system will be developed and trained using data spanning five generations of graduates from one of the departments in the school. The use of artificial intelligence has enabled the development of more sophisticated and more efficient student models which represent and detect a broader range of student behavior than was previously possible.

Due to the platform on which this system will run, it will be developed using the Artificial Intelligence Markup Language, therefore, I will deploy this system using C# for its development.


During the last few years, the application of artificial intelligence in education has grown exponentially, spurred by the fact that it allows us to discover new, interesting and useful knowledge about students. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from an educational context. While traditional database queries can only answer questions such as ”find the students who failed the examinations”, data mining can provide answers to more abstract questions like ”find the students who will possibly succeed in the examinations”. One of the key areas of the application of EDM is the development of student models that would predict student characteristics or performances in their educational institutions. Hence, researchers have begun to investigate various data mining methods to help educators to evaluate and improve the structure of their course context.

The main objective of the admission system is to determine candidates who would likely do well in the university or can perform well within the academic year or to produce students of high grade and intelligence. The quality of candidates admitted into any higher institution affects the level of research and training within the institution, and by extension, has an overall effect on the development of the country itself, as these candidates eventually become key players in the affairs of the country in all sectors of the economy.

Recently, however, there has been a noticeable slide in the quality of graduates of some Nigerian universities. The inadequacies of the present university admission system, among other factors, have been blamed for this decline. Due to the increasing gap between the numbers of students seeking admission and the total available admission slots, there has been a corresponding increased pressure on the process. This pressure has led to rampant cases of admission fraud and related problems.

In Nigeria, students are required to enter secondary school after spending a minimum of six years of Primary Education and passing a prescribed National Common Entrance Examination. A student then spends a minimum period of six years in Secondary School at the end of which he or she takes the General Certificate of Education Examination (GCE), also known as the Senior Secondary Certificate Examination (SSCE) or the Ordinary Level Exams. A maximum of nine and a minimum of seven subjects are registered for in the examination with Mathematics and English Language being compulsory. Nine possible grades are obtainable for each subject; these are A1, A2, A3 (distinctions grades) C4, C5, C6, (credit grades), P7, P8 (pass grades), and F9 (Failure).

Hence this study takes an engineering approach to tackle 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.

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 the said student. It is however obvious that it will be quite difficult to find 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’ backgrounds 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 several factors, and this influence/relationship is not likely going to be any simple known regression model.

An artificial neural network, which 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.


Looking into the institution this day, you will discover that 48% of the student are performing very low on their academic level, whom if asked to defend their admission status cannot (i.e. sitting for the attitude test) when the proper investigation is carried out, findings show 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 are 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. With this little problem, I seek to develop a neural network system an artificial one that will solve this problem. Coupled with the stress gone through during the admission and delay in the process that ends up not being done perfectly to the standard required.


The primary aim of my research work is to develop an artificial neural network system that will be capable of predicting student performance.

Some other objectives which I will be covering in this research work are as follows:

1. A system that will enhance the admission process of this institution in terms of admitting the right student into the institution.

2. An easy and friendly user interface ANNs which will allow fast operation and analysis of student output.

3. To determine some suitable factors that affect a student’s performance.

4. To model an artificial neural network that can be used to predict a candidate’s performance based on given pre-requirement data given to it.


Yes, this system is essential to be developed and implemented in this institution, its significance will promote the image of this university, help the institution in making the proper decision as to whom to admit or knowing one’s capability and performance, implementing this system will encourage any applicant to study in the best atmosphere to bring the best out of him before seeking for admission into this institution. The chances of fraud, bribery, and corruption will be reduced ass each student will be tested by the system and not human. The new system saves and reduces the cost of carrying admission tests or grading student performance.


To achieve this project work I choose for the new system design the following tools which include: unified modeling language, C# as the programming language.

The proposed system design will allow the use of activity diagrams, use cases, data flow diagrams, and a flow chat. This language was chosen because of its wealth of class libraries and features for developing artificial neural systems.


Through an extensive search of the literature and discussion with experts on student performance, several socio-economic, biological, environmental, academic, and other related factors that are considered to influence the performance of a university student were identified. These factors were carefully studied and harmonized into a manageable number suitable for computer coding within the context of the ANN modeling. These influencing factors were categorized as input variables. The output variables on the other hand represent some possible levels of performance of a candidate in terms of the present school grading system.

The Input Variables

The input variables selected are those which can easily be obtained from students’ application/ record cards in the student’s department. The input variables are:

1)UME score,

2)O/level results in Mathematics, English Language, Physics, and Chemistry,

3)Further mathematics,

4)Age of a student at admission,

5)Time that has elapsed between graduating from secondary school and gaining university admission,

6)Parents educational status,

7)Zonal location of student’s secondary school,

8)Type of secondary school attended (privately owned, State or federal government-owned),

9)Location of university and place of residence, and

10)Student’s Gender.

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