Predicting Undergraduate Students Success using Logistic Regression technique

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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

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Outline
3
Introduction

• The concept of Education
• Predictive model
• The factors for Predictive

4
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.

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The Objective
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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

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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.

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Logistic Regression
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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.

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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.

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Methodology
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Results
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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.

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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.

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Thank you