Date of Award


Degree Type


Degree Name

Honors Thesis



First Advisor

Rasitha Jayasekare


The National Survey of Student Engagement (NSSE) surveys students at four-year institutions around the United States in order to offer Universities accessible ways to evaluate their students' experiences and performance. The NSSE data is collected in the form of a Likert-scale survey geared towards first year and senior year students. It asks questions about how they spend their time throughout the academic year and how they rate their experience. This thesis looks at the NSSE survey data from Butler University in 2016 and attempts to apply classification techniques and predictive models to draw conclusions about student performance. Methods such as Multinomial Logistic Regression and Generalized Linear Mixed models are used to identify significant factors affecting students' experiences and performance. These methods, along with Naive Bayes classification and decision tree classification are then used to predict students' grades and experience ratings.

Included in

Mathematics Commons