Student academic performance prediction model using decision tree and fuzzy genetic algorithm

The research on the educational field that involves Data Mining techniques is rapidly increasing. Applying Data Mining techniques in an educational background are known as Educational Data Mining that aims to discover hidden knowledge and patterns about student's performance. This work aims to develop student's academic performance prediction model, for the Bachelor and Master degree students in Computer Science and Electronics and Communication streams using two selected classification methods; Decision Tree and Fuzzy Genetic Algorithm. Parameters like internal marks, sessional marks and admission score were selected to conduct this work. Internal marks are the combination of attendance marks, average marks obtained from two sessional exams and assignment marks. Admission score for degree students is the weighted score obtained from 10th and 12th examination marks and entrance marks. In the case of master's degree students, it includes degree examination marks and entrance marks. Resultant prediction model can be used to identify student's performance for each subject. Thereby, the lecturers can classify students and take an early action to improve their performance. Systematic approaches can be taken to improve the performance with time. Due to early prediction and solutions are done, better results can be expected in fnal exams. Students can view their academic information and updates. Reputed companies having a tie-up with the institution can search students according to their requirements. (C) 2016 The Authors. Published by Elsevier Ltd.