A Review and Performance Prediction of Students’ Using Association Rule Mining based Approach

For the last few decades’ education data mining has become one among foremost promising research areas. The only objective of this area is to explore data mining methods in order to analyze the student performance as well as impart the quality education for enhancing the performance of educational institutes. Data mining is the core part of the knowledge discovery process which is used to extract meaningful information from raw data. However, the various data mining techniques are proposed for achieving the most effective quality results. An Association Rule Mining (ARM) one of the well-known and popular data mining techniques which has been used extensively for educational perspective. In this study, higher education institute i.e. Government Girls College (GGC) data are considered and various attributes regarding student performance are analyzed for study purpose. Therefore, the various experiments based on support and confidence measures like 2%, 4%, 10%, 20% and 40% are conducted to generate interesting rules.  The major objective of this research study is to find the weaker students as well as those students who have bright performance in schooling level but could not be performed well on current semester exams due to certain reasons. However, teachers as well as parents can give particular attention to those students, whether they will perform better in the next semester or exams.