Educational Data Mining Classifier For Semester One Performance to Improve Engineering Students Achievement
In higher education system, it's very important to predict academic performance for students, instructors and management during the course. If institutions able to predict and assess student performance early in the beginning of the course, thus students and instructors ought to take action for better achievement. In order to accomplish such action in Universiti Teknologi Mara that introduces Electrical Engineering Students to fundamental subjects in semester one such as Signal and System, Electrical Circuit, Mathematics and Electronics; this research is to classify students' performance at the beginning of the course to predict students' final achievement. In this study, Neuro Fuzzy classifier system in educational data mining is used to analyze data of semester one and predict students' achievement upon graduation. In order to increase the performance of the system; data pre processing and investigation using linear regression and statistics are used to explore the performance of semester one. By means of applying these methods, students' performance is classified in the beginning of the study, achievement upon graduation is predicted and accuracy is evaluated with actual data.