Unlocking Motivation: Exploring Expectancy and Value to Empower College Women in Data Science Courses

• In Event: Advances in Expectancy-Value-Cost Research

Abstract

This study examines the motivational factors for women in pursuing data science courses in a higher education setting. Based on the Expectancy-Value Theory (EVT), we conducted a thematic analysis of interviews with seven women, and examined their beliefs in personal abilities and perceived value of pursuing data science education. Findings show that participants faced gender-related costs, while also seeing the value of data science as an important resource to navigate male-dominated fields. The study also highlighted instructional features that enhanced women’s expectancy beliefs. This research contributes to the literature by adding details on what could empower women to engage with data science education. These insights could support data science educators in facilitating an inclusive learning environment for women.

Authors

• Jeanne M. McClure, North Carolina State University

• Sunghwan Byun, North Carolina State University