1. Predicting Undergraduate Students Success using Logistic Regression technique Apichai Trangansri, Luddawan Meeanan, Settachai Chaisanit and Anongnart Srivihok Faculty of Information Technology, Sripatum University Chonburi Campus, Thailand

2. Outline IntroductionThe ObjectiveData setLiterature ReviewMethodologyResultsConclusion

3. I NTRODUCTION The concept of Education Predictive model The factors for Predictive

4. I NTRODUCTION The aim of this study was to predictive model for undergraduate students’ success. It provides a manageable structure for the administration of admission of new students and learning management in the institution. Moreover, the predictive model creating knowledge and strategies to improve teaching and learning management in Thailand.

5. The Objective to explore the predicting undergraduate st udent success using Logistic Regression te chnique

6. Population and Dataset Population of this studied was comprised of : –The populations are undergraduate students at Sripatum University Chonburi Campus, Thailand. Dataset: –3,719 dataset

7. Literature Review Forecasting Techniques Forecasting techniques are typically broken into the categories of time series, regression, and subjective techniques. Logistic Regression Logistic Regression analysis is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.

8. Logistic Regression

9. Methodology The methodology of this study comprise of the A logistic regression model was built using data from Sripatum University Chonburi Campus, Thailand. The applicants from the 2001-2011 acadamic years.

10. Methodology Datasets were obtained from the Faculty of Business Administration, Faculty of Accounting and Faculty of Information Technology. The sample group of this study was 3,719 dataset. This research has been divided into 3 classes and 9 variables.

11. Methodology

12. R ESULTS

13. The prediction model divided by faculty show ed that: Faculty of Business Administration: GPA increased by one unit, the students has oppor tunity to graduated 81.5 percent. Faculty of Accounting: GPA increased by one unit, the students has opportunity to graduated 64.3 percent. Faculty of Information Technology: GPA increased by one unit, the students has opportunity to graduated 80.8 percent.

14. Conclusion This research applied of data mining technique for generate Predictive Modeling of undergraduate students success. The research results supported the idea that the ways in which student success can be predicted in conventional and education. Therefore, A logistic regression model was built using data on the applicants from the 2001-2011 acadamic years at Sripatum University Chonburi Campus, Thailand. The result showed that the relationship of variables showed that the data field: major, GPA, age and gender are variables that affect the student succes in significant at 0.05. However, the benefits of predictive model for undergraduate students success. It provides a manageable structure for the administration of admission of new students and learning management in the institution. Moreover, the predictive model creating knowledge and sstrategies to improve teaching and learning management in Thailand.

15. Thank you