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Theories of Learning as Theories of Society: A Contrapuntal Approach to Expanding Disciplinary Authenticity in Computing

Item

Title
Theories of Learning as Theories of Society: A Contrapuntal Approach to Expanding Disciplinary Authenticity in Computing
Abstract/Description
Background
We outline a case for how the Learning Sciences is at a powerful inflection point where the “real world” needs to be seen as comprised of the political entities and processes in which learning happens. We seek to sharpen the principle that learning is political by elucidating historical and contemporary processes of European and U.S. imperialism that remain foundational to our field and by developing the argument that theories of learning are theories of society.

Methods
Through a contrapuntal approach, which emphasizes a critical lens to analyze empire, we juxtapose notions of authentic practice in computing education with scholarship in sociology that brings the lives of tech industry immigrant workers to the fore.

Findings
Our analysis reveals how the social construction of disciplinary and professional expertise in computing is intricately interwoven with historically persistent patterns of the appropriation of the lives and labor of endarkened people through systems of transnational migration and institutional forms of racial segregation.

Contribution
A contrapuntal lens in the Learning Sciences prompts our field to embrace the necessary uncertainties and the theoretical and methodological possibilities that emerge when sites of learning and learning itself are recognized as political and as contestations of empire.
Date
2021
In publication
Journal of the Learning Sciences
Volume
30
Issue
2
Pages
330-349
Resource type
en
Resource status/form
en
Scholarship genre
en
Open access/full-text available
en No
Peer reviewed
en Yes
ISSN
1050-8406
Citation
Philip, T. M., & Sengupta, P. (2021). Theories of Learning as Theories of Society: A Contrapuntal Approach to Expanding Disciplinary Authenticity in Computing. Journal of the Learning Sciences, 30(2), 330–349. https://doi.org/10.1080/10508406.2020.1828089

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