Predicting The Future Academic Performance Of Undergraduate Students With Artificial Neural Networks

Project and Seminar Material for Computer Science and Computer Engineering

Predicting The Future Academic Performance Of Undergraduate Students With Artificial Neural Networks


Abstract


This project is on predicting the feature performances of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in the cause of their first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. We got a result of 80% accuracy from the test which was carried out, with an average error of 0.009781. With this experiment, we have been able to show that future performances of undergraduate student can be predicted, and also the usefulness of being able to predict feature undergraduate results. The result gotten from the test carried out show a high level of accuracy which therefore encourages the use of neural networks for prediction especially in the academic sector.


Chapter One


Introduction

1.1 Background to the Study

Most institutions of higher learning today are concerned with predicting the paths of undergraduate students. By analysing student performances as they move from one level to the next, it is possible to determine which students will join particular course programs and the various fields the students will excel at. Today, one of the biggest challenges that educational institutions face is the explosive growth of educational data and how to use this data to improve the quality of managerial decisions. This research is aimed at using Neural Networks to see how educational data can be made more useful.

Artificial neural network is defined by Dr Robert (1989) an inventor of one of the first neurocomputers as “a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs”. ANNs are processing devices that are modelled based in the neuronal structure of the mammalian cerebral cortex but on rather much smaller scales. A large neural network might be made of thousands of processor units whereas the human brain contains billions of neurons with corresponding increase in magnitude of their overall interaction and emergent behaviour (Maureen, 1989). The motivation of using a neural network approach is its learning algorithm that learns the relationships between variables in sets of data and then builds models to explain these relationships (Radi and Samy, 2013).

Artificial Neural Networks can be used to perform different tasks depending on the training received. With the correct training, neural networks should be able to generalise and should be able to recognize similarities in different input patterns. When designing a neural network, certain parameters must be decided upon, these include:

  1. The number of layers
  2. The number of neurons per layer
  3. The number of training iterations per layer
  4. The number of hidden neurons
  5. The learning rate and
  6. The momentum parameter.

Neural Networks can be applied in the following areas:

  1. Capturing associations or discovering regularities in within a set of patterns;
  2. Where the volume, number of variables or diversity of the data is very great;
  3. Where the relationships between variables are vaguely understood; or
  4. When the relationships are difficult to describe adequately with conventional approaches.

Their advantage over other networks is that they can capture various kinds of relationships therefore allowing the user to quickly and relatively easily model phenomena which may have originally been difficult to explain. Some ANNs are adaptive systems and can for instance be used to model populations and environments which constantly change.

Artificial Neural Networks possess a remarkable ability to derive meaning form complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be noticed by humans or other computer techniques. A well trained neural network can be considered and expert in the range of information it has been given to analyse. Other advantages are;

(a) Adaptive learning:

The ability to learn how to perform tasks based on the data given for training or initial experience.

(a) Self-organisation:

The ability to create its own organisation or representation of the information it receives during learning time.

(b) Real Time Operation:

The computations of an ANN may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.

(c) Fault tolerance through redundant information:

The partial destruction of a network leads to corresponding degradation of performance, however, some of the network capabilities may be retained even with major network damage.

The analysis of educational data is not particularly a new practise. However, latest advances in educational technology as well as increase in computing power and the ability to enter fine-grained data about students use of computer based learning atmosphere have led to increased interest in developing techniques for analysing large amounts of data that are often generated in the educational field.

In this project, artificial neural networks will be explored so as to find meaningful ways to evaluate academic data to aid future decision making so as to improve on academic performances of students.


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:

  1. To collect data of undergraduate students.
  2. To transform the raw data into the suitable format for the prediction tool to be used.
  3. 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

  1. 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.
  2. The network cannot be used if there is only very little data available.

Chapter Five


Summary, Conclusion and Recommendation

5.1 Summary

The main motivation of this project was the increasing rate in poor academic performances of students in recent times. The ability to be able to evaluate and understand trends and patterns in academic data will enable prediction of the students final year result so as to help them change career paths should the need arise.

In this research, the feed forward network was used to evaluate and predict the final year results of undergraduate students to be able to advice the students better on what courses to take in the future and avoid cases of students graduating with poor grades. The feed forward neural network was trained using data retrieved from the computer science department, Federal University of technology Minna. The network uses the 100level course grades of the students, their 100level and 200level CGPA to predict their 500level CGPA. Five samples of the input data were used for testing and a prediction accuracy of 80% was achieved.

The objectives were carried out, and records of students’ grades for five consecutive years were collected to be used for the experiment, although 300level and 400level results were not used. We can therefore say the aim for which this research work was carried out was not defeated.

This project is divided into five chapters with chapter one giving an introduction to Artificial Neural Networks, and chapter two a theoretical background of Artificial Neural Networks. N chapter thee, the experiment is carried out and the results are discussed in chapter four, this chapter (five) is the concluding chapter.


5.2 Recommendations

  1. The limiting factor of this research work lies in the fact that data used was restricted to the computer science department of the federal university of technology minna, therefore future work on an extension of this research to other departments and institutions is recommended.
  2. The current educational system should be reviewed so as to make the necessary changes needed to improve on managerial decisions and produce a higher quality of graduands at the conclusion of subsequent academic sessions.

5.3 Conclusion

The aim of this project was to predicting the future academic performance of undergraduate student using Artificial Neural Networks. Form the results obtained from the experiment that has been carried out, the prediction tool Artificial Neural Networks was able to evaluate the academic data of students and then use the result gotten to predict their final year result. 80% accuracy was recorded from the test carried out, it is therefore safe to say that the aim has been achieved. The school and other higher institutions of learning of learning should also consider adopting neural networks to predict the performance of the students so that they can offer better guidance to these students.


5.4 Further Work

  1. Further evaluation and enhancement of the technique should be carried out in the future to ensure higher accuracy and better results.

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