Full metadata
Title
Developing a Machine Learning Framework for Student Persistence Prediction
Description
Student retention is a critical metric for many universities whose intention is to support student success. The goal of this thesis is to create retention models utilizing machine learning (ML) techniques. The factors explored in this research include only those known during the admissions process. These models have two goals: first, to correctly predict as many non-returning students as possible, while minimizing the number of students who are falsely predicted as non-returning. Next, to identify important features in student retention and provide a practical explanation for a student's decision to no longer persist. The models are then used to provide outreach to students that need more support. The findings of this research indicate that the current top performing model is Adaboost which is able to successfully predict non-returning students with an accuracy of 54 percent.
Date Created
2021
Contributors
- Wade, Alexis N (Author)
- Gel, Esma (Thesis advisor)
- Yan, Hao (Thesis advisor)
- Pavlic, Theodore (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
49 pages
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.161413
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2021
Field of study: Industrial Engineering
System Created
- 2021-11-16 12:54:31
System Modified
- 2021-11-30 12:51:28
- 2 years 5 months ago
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