Semester
Summer
Date of Graduation
2019
Document Type
Dissertation
Degree Type
PhD
College
Eberly College of Arts and Sciences
Department
Physics and Astronomy
Committee Chair
John Stewart
Committee Co-Chair
Gay Stewart
Committee Member
Gay Stewart
Committee Member
Maura McLaughlin
Committee Member
Karen Rambo-Hernandez
Committee Member
Kathleen Koenig
Abstract
Retention of Science, Technology, Engineering, and Mathematics (STEM) students is a serious problem as STEM graduation rates continue to lag the growing demand for the skills taught by these degree programs. Critical to fixing this “leaky pipeline” is investment in improving retention in the first two years of college study and increasing and maintaining the interest of K-12 students in STEM. This thesis will address this in three parts. The first is through evaluation of conceptual tests used to evaluate course improvements to determine the structure student knowledge measured by them. The second part uses machine learning to construct early warning models of student failure in introductory physics courses to aid instructors in better targeting of interventions. The final part assesses the effectiveness of the Pulsar Search Collaboratory, an authentic science experience for middle and high school students, at encouraging K-12 students to pursue STEM degrees.
Recommended Citation
Zabriskie, Cabot Alexander, "Using Machine Learning and Traditional Statistics to Explore Retention and Knowledge Structure in STEM with an emphasis on Physics" (2019). Graduate Theses, Dissertations, and Problem Reports. 4118.
https://researchrepository.wvu.edu/etd/4118