Discovery patterns Influencing Academic performance of Information Technology students -University of Kassala

Abstract

Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students’ data which can be used to discover valuable patterns pertaining students’ learning behavior. This research aim to providing guidance rules that implemented modern methods to help students enhance their academic performance and discover courses that more related with GPA and affect negatively or positively at GPA. This research applied on student’s data from department of information system Faculty of computer science and information Technology University of Kassala (2012 -2018). The study applied two data mining techniques clustering algorithms (k-means) and association rules algorithms this techniques implemented in orange data mining tool to generating strong rules in each study year .The clustering algorithms results were evaluated regarding to high accuracy for each cluster and then applied Association rules algorithms for each cluster. The obtained results are strong rules that use to improve SAP (Student Academic Performance) and appear which courses are effect positively or negatively on accumulative GPA.