Title: Predictive Analytics of Student Graduation Using Logistic Regression and Decision Tree Algorithm

Year of Publication: Dec - 2015
Page Numbers: 41-48
Authors: Ace Lagman, Shaneth Ambat
Conference Name: The Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC2015)
- United Arab Emirates

Abstract:


Educational data mining is an emerging area of data mining application. It is concerned in describing and predicting patterns into huge amount of data usable to educational settings. One main topic in educational data mining is the student graduation. The student graduation rate is the percentage of a school’s first-time, first-year undergraduate students who complete their program successfully. Almost half of first year freshmen enrolled in tertiary level failed to graduate. The colleges and universities consisting of high leaver rates go through loss of fees and potential alumni contributors. This study focused on two aspects; to compare the accuracy rate of different classification algorithms in predicting student graduation and to generate data models that could early predict, and to identify students who are prone of not having graduation on time. The results is use to design proper retention policies and help the student to graduate on-time.