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

Outline

Introduction

The Objective

Data set

Literature Review

Methodology

Results

Conclusion

INTRODUCTION

• The concept of Education

• Predictive model

• The factors for Predictive

INTRODUCTION

• 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.

The Objective

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

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.

Logistic Regression

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.

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.

Methodology

RESULTS

0102030405060708090

100

Prediction model

Faculty of BusinessAdministration

Faculty of Accounting

Faculty of InformationTechnology

RESULTS

• The prediction model divided by faculty showed that: • Faculty of Business Administration: GPA increased

by one unit, the students has opportunity 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.

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.

Thank you