PREDICTING THE FUTURE ACADEMIC PERFORMANCE OF UNDERGRADUATE STUDENTS WITH ARTIFICIAL NEURAL NETWORKS

1.2       STATEMENT OF THE PROBLEM

There is a need for improvement in the service and quality of education in tertiary institutions of learning, the ne1ed to take into consideration the performance of students in each course so as to see how the students can improve in them and in their overall performances. Most times, institutions does not take a look at students' in the previous session before promoting them to the next level or allowing them to continue in a particular course of study. This has caused a great decline in the quality of their final results (CGPA) thereby producing undergraduates with poor grades that also do not meet the necessary requirements for graduation and employment therefore lacking the ability to effectively compete with their counterparts in the wider world.

This research is centred on using Artificial Neural Networks (ANN) to predict students' performances so as to avoid the above mentioned problems thereby making the school counsellors to effectively guide students towards academic excellence.

1.3       AIM AND OBJECTIVES

The aim of this project is to predict the feature academic performance of undergraduate students using Artificial Neural Networks. This is to be achieved by the following objectives:

(i)                 To collect data of undergraduate students.

(ii)               To transform the raw data into the suitable format for the prediction tool to be used.

(iii)             To train the Neural Network with the transformed data using a suitable neural network model.

1.4       SIGNIFICANCE OF THE STUDY

The ability to predict students' feature performances will create a more customised student experience as students will be better advised to enable them improve on their academic performances. It will also enable the lecturers and counsellors determine the strengths and weaknesses of these students as this will enable them understand the students better and know what areas they would fit best in. With predictive ability, failure rates will be greatly reduced.

The groups of people to gain from this are the students, parents and counsellors.

With predictive ability each group involved will have an insight to the students' capabilities as well as changes they need to make to ensure the smooth running of the institution. By so doing, the institution, the lecturers and parents can better advice the students on what career paths to pursue and the student as well are able to make better choices for themselves.

1.5       SCOPE OF THE STUDY

This project is centred on evaluating and predicting students' performances using Artificial Neural Networks (ANN). The grades of the undergraduate students of computer science department, school of information and communication technology will be used for this research. 

1.6       LIMITATIONS

§  This research work covers only the Computer Science Department of the Federal University of Technology Minna, and thus may not be generalizable by other institutions.

§  The network cannot be used if there is only very little data available