Student’s Performance Analysis Using Decision Tree Algorithms

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dc.contributor.authorAbdulsalam, S.O.
dc.contributor.authorSaheed, Y.K.
dc.contributor.authorHambali, M.A.
dc.contributor.authorSalau-Ibrahim, T.T.
dc.contributor.authorBabatunde, A.N.
dc.date.accessioned2025-10-29T11:53:34Z
dc.date.available2025-10-29T11:53:34Z
dc.date.issued2017
dc.description.abstractEducational Data Mining (EDM) is concerns with developing and modeling methods that discover knowledge from data originating from educational environments. This paper presents the use of data mining approach to study students’ performance in CSC207 (Internet Technology and Programming I) a 200 level course in the department of Computer, Library and Information Science. Data mining provides many approaches that could be used to study the students’ performance, classification task is used in this work to evaluate the student’s performance and as there are numbers of approaches that can be used for data classification, including decision tree method. In this work, decision trees were used which include BFTree, J48 and CART. Students’ attribute such as Attendance, Class test, Lab work, Assignment, Previous Semester Marks and End Semester Marks were collected from the students’ management system, to predict the performance at the end of semester examination. This paper also investigates the accuracy of different Decision tree algorithms used. The experimental results show that BFtree is the best algorithm for classification with correctly classified instance of 67.07% and incorrectly classified instance of 32.93%. KEYWORDS: Classification, Decision tree, Students’ Performance, Educational Data Mining
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6275
dc.language.isoen
dc.publisherFaculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania - Annals Computer Science Series Journal, Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania, 15, 55– 62
dc.titleStudent’s Performance Analysis Using Decision Tree Algorithms
dc.typeArticle
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