Evaluation of Student Performance using Data Mining over a Given Data Space
Otobo Firstman Noah1, Baah Barida2, Taylor Onate Egerton3
1Otobo Firstman Noah, Msc Student, Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria.
2Baah Barida, Msc Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria.
3Taylor Onate Egerton, Department of Computer Science, University of Science and Technology, Port Harcourt, Nigeria.
Manuscript received on 21 September 2013 | Revised Manuscript received on 28 September 2013 | Manuscript published on 30 September 2013 | PP: 101-104 | Volume-2 Issue-4, September 2013 | Retrieval Number: D0796092413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The volume of data generated every year in our institutions is enormous, due to this large volume of data there is the need to provide a efficient system support to aid in good decision making process; this is what necessitated this research paper which is all about the evaluation of student performance using data mining technique over a given data space. In this paper we are going to look at the various data mining techniques, data mining algorithms and the k-means clustering technique. In this paper, the performance evaluation of students, were presented using data mining technique and cluster checking. The system examined students who gained admission into the University of Port-Harcourt through the University Matriculation Examination (UME) and through Basic studies programme with the aim of finding out variations in their performance when they graduate from the university. The evaluation was done using data mining technique to find out the ratio that falls into grouping of the grading in the various classes using the cumulative grade point average (CGPA) and the students who failed out. The system was able to cluster, analyze and report the relative performance of each of the groups of the students used in the research work. Finally, the system was implemented using Apache, MySQL, PHP, internet explorer, NetBeans IDE 6.8 and XAMPP web server.
Keywords: Data Mining, Data Space, Clustering, Database
Scope of the Article: Data Mining