Mining of student academic evaluation records in higher education

The various data mining techniques like classification, clustering and relationship mining can be applied on educational data to predict the performance of a student in the examination and bring out betterment in his academic performance. Rule based classification techniques can be used to predict the result of the students in the final semester based on the marks obtained by them in the previous semesters. Decision table and One R rule algorithms are used here to predict the result of the fifth semester student based on the marks obtained by the students in the previous four semesters. The accuracy of the algorithms is analyzed by comparing the prediction given by miner, the algorithm and the result obtained by the students in the fifth semester examination. The performances of the algorithms are analyzed using the true positive, false positive values and the ROC curves.