University Selection Model Using Machine Learning Techniques

Abstract

College admission is a decision that affects the student’s career life. Students have to think about all available options and draw their career path before selecting the college. High-school graduates must commit to several years until graduation before starting careers. University Selection decision depends on student interests, student academic results, student family standard of living, student education language. Students usually consult their friends, school teachers, and family members to select the university; however, sometimes, students after graduation make a career shift and start from the beginning if they make a wrong university selection decision. The research aims to define the factors that affect students’ decision to choose the university and predict student decisions based on testing cases using machine learning techniques. One thousand two hundred applicants were questionnaire. An expert model uses Support Vector Machine (SVM) and Naive Bayes (NB) classifications algorithms’s. Results had shown that students with high school programs (British-IG) use an Individual ecological system. Students with National high school program decisions are affected by their exosystem, while American high school program students’ decisions are affected by their parents and relatives, including their Microsystems. NB had shown better accuracy, recall, and precision values compared to SVM.