Preliminary Research

Current capacity for computational thinking in geography education

Courses with a geo-computation emphasis and focus are beginning to appear in undergraduate geography curricula. A recent survey of highly-ranked undergraduate geography programs in the U.S. found that 80% of departments in their sample offer at least one course involving some type of computer programming (Bowlick, Goldberg & Bednarz, 2017). Yet, much is unknown about the effectiveness of these courses for preparing students for the rapidly evolving job market. Moreover, geographers have voiced intimidation around computer programming, software and web-development because of the perceived steep learning curves associated with these (Muller and Kidd, 2014). Based on that report, it is safe to assume that geography undergraduate students will not enroll in a computer programming courses unless it is required for their degree.

The map below shows the result of preliminary research conducted by the AAG to start assessing the capacity for computational thinking in Geography Programs at the college level.

To assess this capacity, the AAG collected the number of courses listed on 208 geography department’s websites that included words associated with computational thinking in their title[1]. Additionally, the department code of each course was collected, to track whether a course was taught by the department or by another department (e.g., Computer Science department). The map on the left shows the average number of courses per program in each state that includes words associated with computational thinking, regardless of the department coded. The map on the right on the other hand, shows the average number of courses per program in each state that are associated to computational thinking and that have a geography department code, which we assume are “taught” inside the geography department. Not all geography programs are represented in this snapshot of data, which is one of the weaknesses. However, the preliminary results indicate that many students have access to courses that support computational thinking, though not through geography departments directly.

Additionally, sessions were organized at the recent AAG’s Annual Meeting (New Orleans, April 2018) to start discussions among educators, professionals and students. At those workshops, students were disinclined to value the importance of acquiring computational skills. Relevant coursework is perceived as intimidating, and students were resistant to classes that could negatively impact their GPAs. This is consistent with the small fraction of geography programs (10%) that require a computer programming course (or related) for their degree (Bowlick, Goldberg and Bednarz, 2017).

Furthermore, courses that involve computational thinking skills are often taught outside of geography departments, even when they are may be a required program element. Instead, students are often directed to computer science departments and are provided with little guidance or mentoring on which course(s) to take. During these April 2018 workshop discussions, faculty and educators expressed their inexperience and lack of confidence in advising students about computational courses because they themselves lacked or avoided similar coursework when they were students. Two patterns result: some faculty are not willing to teach skills or concepts different from ones they were taught, while others are willing to teach new or unfamiliar skills and concepts but require training and support to do so.

These findings highlight the need to build and expand capacity for computational thinking in undergraduate geography programs and to better understand effective synergies between Computer Science, Engineering, and Geography departments.

[1] Here are a few examples of the words found in course titles that were associated with computational thinking: "programming", "coding", "scripting", "automation", "Netlogo", "Python", "Matlab", "R", "JavaScript", "web mapping".

References

AAG, American Association of Geographers (2017) AAG Disciplinary Data Dashboard. Washington, DC: AAG. Retrieved from: http://www.aag.org/disciplinarydata

AlphaBeta. (2017). The economic impact of geospatial services: how consumers, businesses and society benefit from location-based information. AlphaBeta Online Archive. Retrieved from: http://www.alphabeta.com/wp-content/uploads/2017/09/GeoSpatial-Report_Sept-2017.pdf

Baiocchi, D., & Welser IV, W. (2015). The Democratization of Space: New Actors Need New Rules. Foreign Affairs, 94(3): 98-104.

Bednarz, S. W., Heffron, S., & Huynh, N. T. (Eds.). (2013). A road map for 21st century geography education: Geography education research (A report from the Geography Education Research Committee of the Road Map for 21st Century Geography Education Project). Washington, DC: Association of American Geographers.

Bowlick, F. J., Goldberg, D. W., & Bednarz, S. W. (2017). Computer Science and programming courses in Geography Departments in the United States. The Professional Geographer, 69(1): 138-150.

Chang, J. C. (2002). Women and Minorities in the Science, Mathematics and Engineering Pipeline. ERIC Digest, ERIC Clearinghouse for Community Colleges. Retrieved from: https://eric.ed.gov/?id=ED467855

Couclelis, H. (2003). The certainty of uncertainty: GIS and the limits of geographic knowledge. Transactions in GIS, 7(2): 165-175. DOI: 10.1111/1467-9671.00138

Etherington, T. R. (2016). Teaching introductory GIS programming to geographers using an open source Python approach. Journal of Geography in Higher Education, 40(1): 117-130.

Kwan, M. P. (2002). Is GIS for women? Reflections on the critical discourse in the 1990s. Gender, Place and Culture: A Journal of Feminist Geography, 9(3): 271-279. DOI: https://doi.org/10.1080/0966369022000003888

Levine, R., González, R., Cole, S., Fuhrman, M., & Le Floch, K. C. (2007). The geoscience pipeline: A conceptual framework. Journal of Geoscience Education, 55(6): 458-468. DOI: https://doi.org/10.5408/1089-9995-55.6.458

Mazur, L. B., & Albrecht, J. (2015). Women in the GIS profession. Urban & Regional Information Systems Association Journal, 27(2): 51-64.

Merali, Z. (2010). Computational science: Error, why scientific programming does not compute. Nature, 467(7317): 775-777. DOI: 10.1038/467775a

Muller, C. L., & Kidd, C. (2014). Debugging geographers: teaching programming to non-computer scientists. Journal of Geography in Higher Education, 38(2): 175-192. DOI: https://doi.org/10.1080/03098265.2014.908275

Newcombe, N. (2017). Harnessing Spatial Thinking to Support Stem Learning. OECD Education Working Papers, No. 161, OECD Publishing, Paris, http://dx.doi.org/10.1787/7d5dcae6-en

Oyana, T. J., Garcia, S. J., Haegele, J. A., Hawthorne, T. L., Morgan, J., & Young, N. J. (2015). Nurturing Diversity in STEM Fields through Geography: the Past, the Present, and the Future. Journal of STEM Education: Innovations and Research, 16(2): 20.

Solís, P., & Miyares, I. M. (2014). Introduction: Rethinking practices for enhancing diversity in the discipline. The Professional Geographer, 66(2): 169-172. DOI: https://doi.org/10.1080/00330124.2012.735920

Stephens, M. (2013). Gender and the GeoWeb: divisions in the production of user-generated cartographic information. GeoJournal, 78(6): 981-996. DOI: https://doi.org/10.1007/s10708-013-9492-z

Wikle, T. A., & Fagin, T. D. (2014). GIS course planning: A comparison of syllabi at US college and universities. Transactions in GIS, 18(4): 574-585. DOI: 10.1111/tgis.12048