Abstract
Determining student success as early as possible is an important problem for both students and educational institutions. Educational institutions want to improve retention and graduation rates, whereas students don’t want to sacrifice money and time by leaving the program or extending graduation time. Predicting such success is particularly important for new students in course selection and make an effective and efficient study plan to be successful in the course, and also enables instructors to identify such individuals who might need assistance in the courses and help students to improve their performance in the class. This research examines the most significant factors that impact student success in online master's degree in computer science (OMSCS) program at Georgia Institute of Technology. Student survey results are analyzed using descriptive and inferential analysis methods, random forest and ANOVA. Furthermore, logistic regression is used to predict student’s overall GPA level.
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