Exploring Predictive Factors of Academic Probation Using Data Mining ApproachExploring Predictive Factors of Academic Probation Using Data Mining Approach
- Other Titles
- Exploring Predictive Factors of Academic Probation Using Data Mining Approach
- Authors
- Lee, Hunhee; Kim, Namhyoung
- Issue Date
- Mar-2021
- Publisher
- 대한산업공학회
- Keywords
- Academic Probation; Data Mining Approach; Prediction; Higher Education
- Citation
- Industrial Engineering & Management Systems, v.20, no.1, pp.69 - 81
- Journal Title
- Industrial Engineering & Management Systems
- Volume
- 20
- Number
- 1
- Start Page
- 69
- End Page
- 81
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81785
- DOI
- 10.7232/iems.2021.20.1.69
- ISSN
- 1598-7248
- Abstract
- The purpose of this study is to develop the early prediction model for academic probation to encourage retention of students at universities. For this study, various data from the administration system and learning environment of G University in South Korea were collected. We constructed the predictive model by applying logistic regression to col-lected data using new variables related to campus activities. To solve the class-imbalance problem, we applied data mining techniques. This study is significant in that the model is based on structured real data by using education data mining approach from the academic administrative system we can access. Predictive factors of academic probation were revealed and educational implications of developed predictive were discussed.
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Collections - 사회과학대학 > 응용통계학과 > 1. Journal Articles
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