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Abstract Title: Developing a Python tool to categorize motivation of undergraduate women
Abstract Type: Contributed Poster Presentation
Abstract: Drawing on data collected at the 2015 and 2019 Conferences for Undergraduate Women in Physics, we have analyzed written responses to a prompt asking about women's journeys to majoring in physics. This is the second phase of the a project aiming to better understand the reasons women choose to major in physics. In previous work, we created a coding scheme, based in expectancy-value and self-efficacy theories, and hand-coded survey responses of undergraduate women describing their initial motivation. However, this coding is arduous for large data sets, so we have built a Python tool that takes a survey response asking about motivation as input and outputs the proportion of each motivational code in the response. When we tested this tool in comparison to hand-coding, we found an accuracy of 65%, with higher accuracy for some specific codes such as intrinsic value or interest in astronomy. The accuracy of this tool allows short answer survey data to be categorized relatively quickly. We plan to use this to correlate motivation, along with other survey data, with retention in order to build a full predictive tool for undergraduate retention.
Session Time: Poster Session 3
Poster Number: III-25

Author/Organizer Information

Primary Contact: Maxwell Franklin
Drexel University
Philadelphia, PA 19127
Phone: 5855038778
and Co-Presenter(s)
Dr. Eric Brewe (He/Him), Drexel University
Dr. Annette Ponnock (She/Her), Yale University