Investigating teaching practices in quantitative and computational Social Sciences: a case study

Data education is gaining traction in higher education across disciplines and degree levels. Teaching data skills in the Social Sciences in today's data-driven world is vital for preparing the next generation of data literate and critical social scientists. The ability to identify, assess, analyze, and communicate well and responsibly with data is a skill scholars and professionals need to navigate dynamic and expansive information ecosystems. In response, instructors have adapted their curricula and pedagogy to foster the necessary skills and theoretical knowledge to advance students’ computational and statistical praxis. This paper reports the findings of a local report of a larger national project with other 19 academic participant institutions. It discusses ways academic libraries in association with other campus partners can better support students and teachers in the Social Sciences as they entertain quantitative and computational approaches to deal with pressing contemporary social issues. The study's goals were: 1) Explore pedagogical techniques and support needs in teaching undergraduates with data and 2) Provide actionable recommendations for stakeholders within and outside the library to inform new services, policies, and practices to advance data instruction in the Social Sciences. Interviews were transcribed and coded in MaxQDA. The results of our local assessment revealed that the core learning goal of interviewees is to develop students' critical thinking skills with data, including: 1. A conceptual understanding of the research methods employed by Social Scientists; 2. The ability to critically evaluate research methodologies, findings, and data sets; and 3. Develop prowess using quantitative and computational tools and technologies to aid them in this process. A recurring theme across interviews was students’ fear of math and technology and the challenges it poses to data-related instruction. Instructors value participation in a community of practice and are eager for more institutional support to advance their own computational skills.