Ascertaining the Factors Influencing Students' Performance for Engineering Pedagogy

Abstract

The education domain offers ground for many interesting and challenging data mining applications like astronomy, chemistry, engineering, climate studies, geology, oceanography, ecology, physics, biology, health sciences and computer science. We study the application of data mining to educational data collected from Guru Nanak Institutions, Hyderbad, India.

We applied very distinctive techniques like association rule and classification algorithms. This work presents an approach for classifying students in order to predict their final grade based on features extracted from student's data in an educational system. This application can help both educators and students, and improve their quality of work. Finally, we analyze the distribution of information across students, and identify factors that predict the number of successful (pass) students.