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Connecting Online Work and Online Education at Scale
University of California, Berkeley.
Karlsruhe Institute of Technology.
Harvard University, Cambridge, MA.
Google, Inc., San Francisco.
Show others and affiliations
Number of Authors: 62016 (English)In: CHI EA '16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, New York: ACM Digital Library, 2016, p. 3536-3541Conference paper, Published paper (Refereed)
Abstract [en]

Education is one of the eight Millennium Development Goals (MDG) of the United Nations. Considerable interest has been displayed in online education at scale, a new arising concept to realize the MDG. Yet connecting online education to real jobs is still a challenge. This CHI workshop bridges this gap by bringing together groups and insights from related work at HCOMP, CSCW, and Learning at Scale. The workshop aims at providing opportunities for groups not yet in the focus of online education, exemplified by low SES and less educated students who have not have equal access to higher education, compared to typical students in MOOCs. The focus is on theoretical and empirical connections between online education and job opportunities which can reduce the financial gap, by providing students with an income during their studies. The workshop explores the technological analogue of the concept of "apprenticeship", long established in the European Union, and education research (Collins, Seely Brown, Newman, 1989). This allows students to do useful work as an apprentice during their studies. This workshop tackles such questions by bringing together participants from industry (e.g., platforms similar to Upwork, Amazon Mechanical Turk); education, psychology, and MOOCs (e.g., attendees of AERA, EDM, AIED, Learning at Scale); crowdsourcing and collaborative work (e.g., attendees of CHI, CSCW, NIPS, AAAI's HCOMP).

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2016. p. 3536-3541
National Category
Business Administration
Research subject
Industrial Marketing
Identifiers
URN: urn:nbn:se:ltu:diva-27152DOI: 10.1145/2851581.2856488Scopus ID: 2-s2.0-85014640511Local ID: 07d5bc44-b339-4d67-aeb7-1037ef739205ISBN: 978-1-4503-4082-3 (print)OAI: oai:DiVA.org:ltu-27152DiVA, id: diva2:1000333
Conference
CHI Conference Extended Abstracts on Human Factors in Computing Systems : 07/05/2016 - 12/05/2016
Note

Godkänd; 2016; 20160519 (andbra)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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