Empirical Study of Decision Tree and Artificial Neural Network Algorithm for Mining Educational Database

The ability to predict student’s performance is very important in educational environments because it plays an important role in producing the best quality graduates and post-graduates who will become great leaders of tomorrow and source of manpower for the country. Therefore the performance of students in universities is of utmost concern. One way to achieve this is by discovering knowledge for prediction as regards enrollment of student in a particular course, prediction of students’ performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Over the years, many students who enrolled in University of Ibadan M.Sc. program were unable to complete the program because there were no supporting tools that can help them take the best decision previous to their enrolment. Some also finish with poor grades, due to the fact that the students enrolment is only based on their personal experience. However, many students do not have enough experience for taking enrolment decisions. This is a waste of resources from the student’s point of view as well as from the department’s. These students also have probably wasted their time doing a course that they do not have the ability to do or interest to complete the program. On the other hand the department has wasted resources on such students. These resources could have been applied elsewhere or used on for student that were not admitted but deserved admission. The aim of this research work is to use Data Mining techniques to study students’ performance in order to discover appropriate knowledge and extract useful patterns from existing stored data of students. The knowledge and pattern extracted would be used for decision making and the specific Objectives are to discover knowledge for prediction regarding enrolment of student in a particular course and enhance decision making, to improve students’ performance and overcome the problem of low grades of graduate students and to discover an efficient algorithm that is sufficient in handling mining of data in educational sector. The work investigates the educational domain of data mining using a case study of the M.Sc. Student’s data from Computer Science department, University of Ibadan. The data comprised of four hundred and eleven (411) records of students. In this research, the classification task is used to evaluate student’s performance and as there are many approaches that are used for data classification, the neural network and decision tree method was used. The results of the two classification methods - Decision Trees and Neural Network are compared to determine the one that gives the best classification results as well as prediction capability in EDM. For the modeling stage, an open source software called WEKA 3.7. 9 was used. The data set was divided into two sets – Training and Testing. Seventy percent (70%) was used for training while thirty percent (30%) was used for testing. From the output generated from the experiment, for neural network, as the number of hidden layer increases, a better result was obtained. The results obtained from the analysis clearly demonstrated a superior performance of neural network over decision tree not only in terms of the number of correctly classified instances but also in terms of RMSE, MAE, RAE. Neural Network performed well in classification as well as in prediction but suffered from lack of speed. Decision Tree was fast but performed badly at the classification. Also the rules generated makes decision tree to be clearer and understandable. Neural Network gives the best classification results as well as prediction capability in EDM.