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Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development

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Teaching on Jupyter : Using notebooks to accelerate learning and curriculum development. / Reades, Jon.

In: REGION, Vol. 7, No. 1, 23.03.2020, p. 21-34.

Research output: Contribution to journalArticle

Harvard

Reades, J 2020, 'Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development', REGION, vol. 7, no. 1, pp. 21-34. https://doi.org/10.18335/region.v7i1.282

APA

Reades, J. (2020). Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development. REGION, 7(1), 21-34. https://doi.org/10.18335/region.v7i1.282

Vancouver

Reades J. Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development. REGION. 2020 Mar 23;7(1):21-34. https://doi.org/10.18335/region.v7i1.282

Author

Reades, Jon. / Teaching on Jupyter : Using notebooks to accelerate learning and curriculum development. In: REGION. 2020 ; Vol. 7, No. 1. pp. 21-34.

Bibtex Download

@article{3be5d359fa584b0c89e9a27d3774b547,
title = "Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development",
abstract = "The proliferation of large, complex 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 the 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.",
author = "Jon Reades",
year = "2020",
month = "3",
day = "23",
doi = "https://doi.org/10.18335/region.v7i1.282",
language = "English",
volume = "7",
pages = "21--34",
journal = "REGION",
number = "1",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Teaching on Jupyter

T2 - Using notebooks to accelerate learning and curriculum development

AU - Reades, Jon

PY - 2020/3/23

Y1 - 2020/3/23

N2 - The proliferation of large, complex 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 the 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.

AB - The proliferation of large, complex 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 the 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.

UR - http://www.scopus.com/inward/record.url?scp=85082804372&partnerID=8YFLogxK

U2 - https://doi.org/10.18335/region.v7i1.282

DO - https://doi.org/10.18335/region.v7i1.282

M3 - Article

VL - 7

SP - 21

EP - 34

JO - REGION

JF - REGION

IS - 1

ER -

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