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.
Details
Title
- Developing a Machine Learning Framework for Student Persistence Prediction
Contributors
- Wade, Alexis N (Author)
- Gel, Esma (Thesis advisor)
- Yan, Hao (Thesis advisor)
- Pavlic, Theodore (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2021
Subjects
Resource Type
Collections this item is in
Note
- Partial requirement for: M.S., Arizona State University, 2021
- Field of study: Industrial Engineering