Understanding Computer Science Academic Performance Using Principal Components Analysis
- ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
- Christopher Smith (Creator)
- Institution
- Appalachian State University (ASU )
- Web Site: https://library.appstate.edu/
- Advisor
- R. Mitchell Parry
Abstract: Some students perform better in school than others. Some classes are also harder than others. This thesis poses the question: Are there types of students that do better in certain types of classes? We model student grades as a combination of class difficulty, student GPA, and student-class preference using student transcript data for Computer Science undergraduates at Appalachian State University. This thesis applies principal components analysis to relate classes to each other, interprets these relationships, and quantifies their importance for grade estimation.
Understanding Computer Science Academic Performance Using Principal Components Analysis
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Created on 1/29/2018
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Additional Information
- Publication
- Thesis
- Smith, C. (2017). Understanding Computer Science Academic Performance Using Principal Components Analysis. Unpublished Master’s Thesis. Appalachian State University, Boone, NC.
- Language: English
- Date: 2017
- Keywords
- Machine Learning, Principal Components Analysis