Improving Student Learning Outcomes Using Data Mining

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Students’ success is an important issue for university policy-makers worldwide due to the negative image of high dropouts. This research study aims to apply educational data mining techniques to investigate the demographic features of at-risk, failure and successful students groups in Gulf University for Science and Technology by using students’ data to provide reference for university decision-makers to understand these groups and form policies related to students’ academic success. In particular, this research paper intends to identify which students are likely to be at risk, who perform poorly in classrooms, and therefore will fail or drop-out. The research will analyze students’ historical data for the academic years 2008-2009 to 2012-2013, in order to identify relationships within the students’ data that point to processes and circumstances that lead to students’ low achievement or failure. The demographic characteristics of students, such as gender, age, marital status, high school diploma GPA, major of high school diploma (sciences/arts), type of high school (public/private), funding type (scholarship/private), and studying habits will be investigated to predict students’ success.