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  Prediction of Student's Performance using Selected Classification Methods: A Data Mining Approach  
  Authors : Abba Babakura; Abubakar Roko; Aminu Bui; Ibrahim Saidu; Jobson Ewalefoh
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Educational Data Mining (EDM) research have emerged as an interesting area of research, which are extracting useful knowledge from educational databases for purposes such as predicting student's success. The extracted knowledge helps the institutions to improve their teaching methods and learning process. In this paper, we applied Decision Tree, Na´ve Bayes and Neural Network classification methods for predicting the student's performance based on the grade level. This aim to resolve the problem of difficulty in predicting the performance of student's in institutions. The objectives of this paper are to (i) implement three classification methods independently on the student's performance dataset, and (ii) determine the best method among the three classification methods. The results shows that the Decision Tree produces the highest accuracy rate of 77.778%, followed by the Neural Network with accuracy rate of 70.886% and the Na´ve Bayes produces the lowest at accuracy rate 66.865%. The result recommends that Decision Tree is used in predicting student's performance rather than Na´ve Bayes and Neural Network.


Published In : IJCSN Journal Volume 8, Issue 3

Date of Publication : June 2019

Pages : 276-284

Figures :07

Tables : 05


Abba Babakura : Computer Science Unit, Department of Mathematics, UDUS, Sokoto, Nigeria.

Abubakar Roko : Computer Science Unit, Department of Mathematics, UDUS, Sokoto, Nigeria.

Aminu Bui : Computer Science Unit, Department of Mathematics, UDUS, Sokoto, Nigeria.

Ibrahim Saidu : Computer Science Unit, Department of Mathematics, UDUS, Sokoto, Nigeria.

Jobson Ewalefoh : Department of Political Science, UNISA, South Africa.


Educational Data Mining, Prediction, Student performance, Decision Tree, Neural Network and Na´ve Bayes

In this research, an effort is made to find the impact of our proposed features and models on student's performance prediction. Predictions of student performance can be useful in many contexts. In this work, some feature sets are identified that significantly affect the performance of each student and grade level of student's at the end of academic year are predicted. The student's performance dataset is used to experimentally evaluate the performance of three classification methods. We implemented and tested with test counts for each of the methods and obtain the classification results. The classification results shows that the Decision Tree produces higher accuracy rate of 77.778%, followed by the Neural Network with an accuracy rate of 70.886% and the Na´ve Bayes produces the lowest with an accuracy rate of 66.865%.


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