Introducing Computing Concepts to Non-Majors

Bort, Heather; Czarnik, Mimi; Brylow, Dennis

doi:10.1145/2676723.2677308

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…We have also come to believe that the use of Jupyter-like platforms in non-STEM disciplines may have a role to play in addressing a deeper problem: the widening participation challenge in computationally-oriented disciplines such as data science (The Royal Society 2019). A particular contribution is these other disciplines' capacity to provide an applied context for computational training that helps to motivate further study and engagement (see Bort et al 2015, for a creative application in literary studies). It should not be the responsibility of Geography and allied fields to plug the so-called 'leaky pipeline' (Berryman 1983), but they may yet create novel pathways for a more diverse cohort of students to enter computationally intensive fields.…”

Section: And Back To the Futurementioning

confidence: 99%

Teaching on Jupyter

Reades

2020

REGION

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The proliferation of large, complex data spatial data sets presents challenges to the way that regional science—and geography more widely—is researched and taught. Increasingly, it is not ‘just’ quantitative skills that are needed, but computational ones. However, the majority of undergraduate programmes have yet to offer much more than a one-off ‘GIS programming’ class since such courses are seen as challenging not only for students to take, but for staff to deliver. Using evaluation criterion of minimal complexity, maximal flexibility, interactivity, utility, and maintainability, we show how the technical features of Jupyter notebooks—particularly when combined with the popularity of Anaconda Python and Docker—enabled us to develop and deliver a suite of three ‘geocomputation’ modules to Geography undergraduates, with some progressing to data science and analytics roles.

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Section: And Back To the Futurementioning

confidence: 99%

Teaching on Jupyter

Reades

2020

REGION

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“…In effect, in both of the example use cases the role of the instructor is to provide an integrative narrative that guides the students and contextualises their choice of components. We think that this has the potential to free up instructors to focus on where they can most effectively 'add value'; not in developing yet another way to show how a list or dictionary works, but in explaining why they matter to a geography, political science, or literature major (see Bort et al 2015 for an application in the literature).…”

Section: Engagementmentioning

confidence: 99%

Geographical Python Teaching Resources: geopyter

Reades

Rey

2021

J Geogr Syst

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Abstractgeopyter, an acronym of Geographical Python Teaching Resources, provides a hub for the distribution of ‘best practice’ in computational and spatial analytic instruction, enabling instructors to quickly and flexibly remix contributed content to suit their needs and delivery framework and encouraging contributors from around the world to ‘give back’ whether in terms of how to teach individual concepts or deliver whole courses. As such, geopyter is positioned at the confluence of two powerful streams of thought in software and education: the free and open-source software movement in which contributors help to build better software, usually on an unpaid basis, in return for having access to better tools and the recognition of their peers); and the rise of Massive Open Online Courses, which seek to radically expand access to education by moving course content online and providing access to students anywhere in the world at little or no cost. This paper sets out in greater detail the origins and inspiration for geopyter, the design of the system and, through examples, the types of innovative workflows that it enables for teachers. We believe that tools like geopyter, which build on open teaching practices and promote the development of a shared understanding of what it is to be a computational geographer represent an opportunity to expand the impact of this second wave of innovation in instruction while reducing the demands placed on those actively teaching in this area.

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“…A growing body of work explores some of the interdisciplinary applications of CT. For example, Bort, Czarnik, & Brylow [1] recently explored how data analysis and visualization techniques could be applied in an English literature class in order to help students better understand contextual information, analyze characters' social networks, and visualize literary techniques. Through this course, researchers saw "analytical and problemsolving abilities develop through these computational approaches...and observed a growing awareness [on the part of students] of the importance and usefulness of analyzing literature as data, as well as data in literature" (p. 137).…”

Section: Background and Related Workmentioning

confidence: 99%

Computer Science Meets Social Studies

Wart

2015

Proceedings of the Eleventh Annual International Conference on International Computing Education Research

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Data science is an emerging 21 st century literacy that promises to support learning in a wide variety of disciplines. It also provides an engaging context in which to learn computational thinking skills in existing classroom contexts. For my dissertation research, I will explore whether and how data science can support inquirybased learning in social studies. I conjecture that data science could provide students with an opportunity to use computing to better understand their social world, while also allowing students to draw from and make sense of their own experiences. I plan to explore this idea by enhancing and evaluating a software tool, Local Ground, and creating a curriculum that helps young people identify and describe salient characteristics of their lived realities, moving from unstructured to structured data representations (e.g., photos to tags to databases). This process may generate a rich, motivating data source to explore subsequent ideas in computational thinking and social science.

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Introducing Computing Concepts to Non-Majors

Cited by 7 publications

References 24 publications

Teaching on Jupyter

Teaching on Jupyter

Geographical Python Teaching Resources: geopyter

Computer Science Meets Social Studies

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