To increase diversity and representation in math and statistics and in STEM at large, it is essential that undergraduate students are able to envision themselves as contributing members of these fields. Therefore, it is crucial to understand the student experience in introductory quantitative science courses, as it is there that many students first assess whether they can successfully pursue a particular STEM major or career.
We will present mixed-methods research on undergraduate students’ sense of community and belonging in in-person, hybrid, and online introductory math and statistics courses at Duke University from Fall 2020 through Spring 2022. The project’s goals are threefold:
To identify pedagogies, policies, and structures in introductory quantitative science courses which correlate with an increased sense of classroom community;
To better understand how students in introductory quantitative science courses perceive and experience classroom community; and
To analyze how these perceptions and experiences differ across a diverse population, including along demographic axes and different learning modalities and preferences.
Our presentation will include preliminary results from our quantitative analysis of survey data gathered during the 2020-21 school year (from over 300 students in 21 sections of 9 different courses), as well as a discussion of the ongoing mixed-methods research we are conducting during the current school year. Quantitative data comes from administration of Cho and Demmans Epp’s 2019 short-form adaptation of Rovai’s Classroom Community Scale, which we validated for use with an undergraduate population, and are analyzed using structural equation modeling. Preliminary results indicate a positive correlation between group projects and student sense of community, as well as a difference in sense of belonging across racial self-identifications. Qualitative data comes from ongoing focus groups, and our presentation will include a discussion of the creation and refinement of the protocol being used, as well as the analysis.