Data Across the Curriculum: The Explanatory Power of Data in Global Development & Geography

The field of geography is split into two camps: critical scholars, who are skeptical of data because they believe it silences certain voices within society and fails to explain process and context, and empirical scholars, who incorporate data to create empirical models that explain geographic concepts and trends.  Leif  Brottem, Assistant Professor of Political Science with a PhD in Geography, is a firm believer in the importance of both critical and empirical approaches.  Data analysis can compensate for and expand upon the limits of text and qualitative evidence. His focus on data analysis as a tool that illustrates narrative is evident in the work of each of his three classes, Introduction to Global Development Studies (GDS), Introduction to Geographical Analysis and Cartography, and Climate Change, Development, and Environment.

In his Introduction to Global Development Studies, for instance, Brottem utilizes infographics & charts to explain basic concepts, and utilizes data tools such as GapMinder to illustrate change over time and regional differences pertaining to a variety of development indicators. His students also complete two data analysis exercises as a part of the class: one exercise asks students to study the relationship between economic development and social development indicators, and the second has students explore different aspects of population dynamics such as carrying capacity, limits to growth and the determinants of population growth.

In Brottem’s Introduction to Geographical Analysis and Cartography course, students learn both the basic critical perspectives on how to evaluate maps and understand their overt and covert messages and practical techniques for making maps using Geographical Information Systems software.  Students complete in-class exercises and take-home labs that require creating data and using data to solve problems.

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Finally, in Climate Change, Development, and Environment, Brottem utilizes data analysis in the form of topic-modeling: students investigate textual trends in various sources, from tweets to scholarly articles, using the MALLET topic model package. In addition, his students also work with Nvivo to conduct further qualitative analysis, and GIS to visualize spatial trends.

Working with data builds data literacy, a marketable and necessary skill in the real world that Brottem says isn’t typically developed in a liberal arts settings. Building data literacy is especially important in his introductory classes, because he has students who wouldn’t otherwise be exposed to data, and aims to get them comfortable with using data and reduce their fears of data, numbers, and data analysis. Brottem strongly believes that data is a powerful explanatory tool that helps students think of different ways to look at the world and their studies, beyond theory.

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Data Across the Curriculum: Using Geospatial Data to Illustrate Historical Change

History is a discipline that is founded on looking at changes over time, and for Sarah Purcell, Professor of History, data is an essential tool in measuring that change. More specifically, Purcell employs geospatial data to investigate historical change in both time and space for her Civil War & Reconstruction class, which focuses on the causes, progress, and consequences of the Civil War and Reconstruction with an emphasis on race, politics, economics, gender, and military conflict.

Purcell uses a stair-step approach in getting students exposed to geospatial data, first by using Google Maps to compare Civil War battleground locations to the locations of students’ hometowns, then investigating how other historians have used data, especially economic and demographic data, in tandem with historical narrative. Finally, Purcell has her students work with ArcGIS, an analytical map-making software, to visualize geographic trends in various historical data. For example, students in the class explore on black soldiers who enlisted in the U.S. Army during the Civil War in an in-class exercise (Figure 1) that encourages them to think critically about military data.

 

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To Sarah Purcell, data is important due to its wide applicability: using data in the context of history teaches a valuable lesson about how data can enhance just about any discipline. Moreover, in the history field, there exists a broad array of different types of data to be utilized, both qualitative and quantitative. While Purcell admits that some students have easier facility with working with data than others, she stresses that the struggle is important in internalizing quantitative literacy and getting accustomed to confronting data, an essential skill. The amount of involvement with data students get in her courses has impacted her students in a variety of ways: some students have gone on to get further training in ArcGIS via formal coursework, and others have been able to secure jobs, citing that employers are largely attracted to data skills in historical work.

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Data Across the Curriculum: Teaching Data Skills in Sociology

Casey Oberlin, Assistant Professor of Sociology, understands the importance of using data in the classroom, especially in such a discipline as Sociology, which is commonly viewed by others outside the discipline as a field with less real-life application of hard skills (e.g. data analysis). This conception is far from the truth, and Oberlin’s approach with data in the classroom gives her students a very holistic and interactive view of data analysis in the field that shows how data is part and parcel to the discipline.
Oberlin uses both her introductory Sociology courses and Research Methods courses as opportunities for students to get deeply entrenched with the data-rich, multi-tiered research process of the field. Data in Sociology is very diverse, as it involves both quantitative and qualitative measures, so Oberlin’s approach focuses on getting students exposed to the vast array of data types, as well as the techniques, technologies, and methods used to interpreting each type.

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At the introductory level, Oberlin focuses on data consumption as a first step to data concepts. Students study infographics (see Figure 1) and other data visualizations to learn how to present data and interpret the data being presented. Oberlin’s Research Methods courses are reserved for her experiential-based approach with data that teaches students two data software programs throughout the semester, one quantitative (SPSS) and the other qualitative (Nvivo), shows students the wide range of data utilized by Sociology, and has students grapple with the entire research process for themselves. In Research Methods, students create research questions, hypotheses/expectations, clean or assess the dataset, analyze their results, and present their work in a professional manner. Her heavy guidance through the research process helps to mitigate understandable anxiety about trying new techniques and presenting their ongoing work, setting her students up to then develop their own sustained research project throughout the semester. Oberlin states this immersive method is beneficial to and enthusiastically received by students, as the practice in research opens doors to internships, jobs, and grad schools.

All in all, Casey Oberlin’s utilization of data in the class gives students exposure to the intensive research process that is integral to Sociology and teaches important data skills and concepts that are applicable both in the real-world and in a classroom setting.

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Data Across the Curriculum: Using Qualitative Data Analysis in Teaching Spanish

When Spanish Professor Pérez incorporates NVivo, a qualitative research tool, into her teaching of Spanish, she sees it as a way to prepare her students for their future careers. Based on the trajectory of the field, she believes that “the digital humanities are here to stay.” While she realizes that not every student that studies Spanish plans on a career in academia or as a Spanish teacher, she hopes that working with digital technology will prepare her students to adapt to a variety of digital research tools in a wide range of fields.

After learning about NVivo, Professor Pérez decided to try using the program in her own research on festival books. Her initial project included only a small number of texts; however, with NVivo’s capacity for large-scale comparison between digital texts, her project has expanded to include around 700 texts.

Once she was familiar with NVivo, Professor Pérez decided to include a short assignment using the program in her Spanish seminar focused on Miguel de Cervantes’ classic novel, Don Quijote.

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SaraSanders ‘14, the 2014-15 DASIL Post-Baccalaureate Fellow, gave an introductory workshop in the class, and Professor Pérez assigned three chapters of the Quijote to each small group of students to analyze digitally. Students then produced reports that included their analytical findings and reflections on NVivo’s usefulness.

So far, Professor Pérez has noted differences in how students respond to NVivo: the majority of her science-major students critiqued the program, wishing that it included detailed quantitative analysis, while humanities majors were usually complimentary. Eventually, she hopes to share further observations about the connection between digital technology and pedagogy at conferences and in a published article. As one of the first professors in Grinnell’s Spanish department to utilize digital analysis in her classes, she also hopes that her experiences with the developing field of digital humanities will facilitate other professors’ explorations of new technologies.

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This past summer, Professor Pérez received a Steven Elkes Grant to develop the use of technology in a new course.  With the help of her research assistant, Alex Claycomb ’18, she is in the process of designing a course entitled “Designing Empire: Plazas, Power and Urban Planning in Habsburg Spain and its Colonies,” which integrates two new NVivo assignments as well as work with GIS and mapping.

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Data Across the Curriculum: Integrating Data Analysis with Narrative in Political Science

From a pedagogical standpoint, Danielle Lussier, Assistant Professor of Political Science, stresses data as a tool for helping students approach problems from multiple perspectives. Working interactively with data allows them to better compare narratives and better understand the research process in both lower-level and upper-level material.

Political science is both a quantitative and qualitative field, so students at all levels of Lussier’s political science classes delve into both data types extensively and build data analytic skills as students progress in the major. Every class taught by Lussier involves data labs that draw on both cross-national data with countries as the unit of measure and on data with individuals as the unit of measure. The labs directly relate to readings, concepts, and/or countries that students study.

At the 100-level, students gain both an introduction to fundamental data concepts such as the construction and measurement of variables and to analytical computer programs like STATA, a statistical package, and ArcGIS, which analyses spatial data. The image below is of a GIS map her introductory political science students make in a data lab.

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At the 200-level, Lussier’s students delve into applied data analysis and write in-depth data reports that compare data analyses from the course readings to data analyses that students reconstruct and update from the readings.

At the 300-level, students get the opportunity to pose questions about class readings and use lab time to test their inquiries with actual data from the readings. In addition, Lussier assigns students research modules that allow them to create their own qualitative variables from cross-national data that they then transform into quantitative data, giving students the opportunity to apply the data skills they’ve accumulated in each course level.

The positive impact of incorporating data into classroom work is not lost on students. Students in all levels of her courses are widely receptive to data in coursework and have viewed working with data in her classes as an integral stepping stone to both academic and professional pursuits. Adam Lauretig ’13, the first Post-Baccalaureate Fellow for DASIL, was inspired by Lussier’s data-driven coursework to pursue more advanced courses in spatial statistics, and subsequently created visualizations like the interactive timeline map of historical coups d’etat. Additionally, many of her students have cited the research and data skills developed in her class work as marketable to employers and graduate programs.

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