|International Journal of Computer Applications
|Foundation of Computer Science (FCS), NY, USA
|Volume 116 - Number 1
|Year of Publication: 2015
|Authors: Omprakash Chandrakar, Jatinderkumar R. Saini
Omprakash Chandrakar, Jatinderkumar R. Saini . Predicting Examination Results using Association Rule Mining. International Journal of Computer Applications. 116, 1 ( April 2015), 7-10. DOI=10.5120/20298-2330
Higher education has changed a lot in the last decade. The use of various innovative techniques and technologies, especially ICT in teaching learning process is increasing day by day. Number of information systems has been developed and successfully implemented to support educational processes. These systems typically capture almost every data regarding a student, right from their enrollment into a course to graduation and placement. If these data are analyzed and visualize properly, can provide valuable knowledge that can be used to enhance their learning skill and to predict threats if any, well in advance so that appropriate measure can be taken to avoid it. Knowledge Discovery in Database (KDD) is usually referred as Data mining. It is a process of extracting new and potential useful information from large databases. Data mining tools are used to identify any pattern or predict future trends and behaviors. This enables decision maker to make proactive and knowledge-driven decisions. This paper presents an application of data mining in higher education. Association rule mining is applied to analyze the performance of students in their examinations and predicts the outcome of the forthcoming examination. This prediction allows student and teacher to identify the subjects which need more attention even before the commencement of semester.