GDL

Efficiency of Decision Trees in Predicting Student's Academic Performance

ISSN : 2231 - 5403
Journal from gdlhub / 2017-08-14 11:52:33
By : S. Anupama Kumar and Dr. Vijayalakshmi M.N, Computer Science & Information Technology
Created : 2012-06-26, with 1 files

Keyword : Assessment, Prediction, Educational data mining, Decision tree, C4.5algorithm, ID3 algorithm
Subject : Efficiency of Decision Trees in Predicting Student's Academic Performance
Url : http://airccj.org/CSCP/vol1/csit1230.pdf
Document Source : Internet

Educational data mining is used to study the data available in the educational field and bring


out the hidden knowledge from it. Classification methods like decision trees, rule mining,


Bayesian network etc can be applied on the educational data for predicting the students


behavior, performance in examination etc. This prediction will help the tutors to identify the


weak students and help them to score better marks. The C4.5 decision tree algorithm is applied


on student’s internal assessment data to predict their performance in the final exam. The


outcome of the decision tree predicted the number of students who are likely to fail or pass. The


result is given to the tutor and steps were taken to improve the performance of the students who


were predicted to fail. After the declaration of the results in the final examination the marks


obtained by the students are fed into the system and the results were analyzed. The comparative


analysis of the results states that the prediction has helped the weaker students to improve and


brought out betterment in the result. To analyse the accuracy of the algorithm, it is compared


with ID3 algorithm and found to be more efficient in terms of the accurately predicting the


outcome of the student and time taken to derive the tree.

Description Alternative :

Educational data mining is used to study the data available in the educational field and bring


out the hidden knowledge from it. Classification methods like decision trees, rule mining,


Bayesian network etc can be applied on the educational data for predicting the students


behavior, performance in examination etc. This prediction will help the tutors to identify the


weak students and help them to score better marks. The C4.5 decision tree algorithm is applied


on student’s internal assessment data to predict their performance in the final exam. The


outcome of the decision tree predicted the number of students who are likely to fail or pass. The


result is given to the tutor and steps were taken to improve the performance of the students who


were predicted to fail. After the declaration of the results in the final examination the marks


obtained by the students are fed into the system and the results were analyzed. The comparative


analysis of the results states that the prediction has helped the weaker students to improve and


brought out betterment in the result. To analyse the accuracy of the algorithm, it is compared


with ID3 algorithm and found to be more efficient in terms of the accurately predicting the


outcome of the student and time taken to derive the tree.

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