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Understanding the Practices and Challenges of Teaching with Data in Undergraduate Social Science Courses at Carnegie Mellon University

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Over the past several years, the concept of data has permeated every aspect of life. While in the past some might have erroneously assumed that data analysis or data manipulation was solely for people engaged in the “hard” sciences, it is now widely recognized that people from the social sciences and humanities are incorporating data analysis into their work. Each human has essentially become a walking dataset in our increasingly digital world, which makes understanding how to work with and critically interpret data increasingly more important. Social scientists have increasingly picked up the charge of fostering smart data practices in research, both in terms of using qualitative and quantitative data. However, while these practices are becoming more commonplace, there are still many aspects of data use or analysis that remain confusing to some students in social science courses. It is important for us as librarians who serve this community to better understand how our instructors in the social sciences at Carnegie Mellon University (CMU) explain how to use data and its associated concepts, so that we in turn can provide better support to these communities.

The general audience for this report includes anyone who is interested in supporting data literacy in undergraduate social science courses whether as the instructor or in a supporting role (such as librarians and/or consultants). This report is organized in the following manner: first, we provide an overview of the methods enlisted during the course of this project, including the theoretical methodology, recruitment of participants, and the data analysis process. Next, we detail the major themes and sub-themes which arose from our analysis of the interview content. Then, based on our findings in the interviews, we provide several recommendations for educators (including course instructors and/or librarians) on how to best support data literacy initiatives in undergraduate social science settings. Finally, we close the report with our conclusions and possible future directions for this research.