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  • 3389476

    Final published version, 232 KB, PDF-document

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Massive Open Online Courses (MOOCs) aim to educate the world, especially learners from developing countries. While MOOCs are certainly available to the masses, they are not yet fully accessible. Although all course content is just clicks
away, deeply engaging with a MOOC requires a substantial time commitment, which frequently becomes a barrier to success. To mitigate the time required to learn from a MOOC, we here introduce a design that enables learners to earn money by applying what they learn in the course to real-world marketplace tasks. We present a Paid Task Recommender System (Rec-$ys), which automatically recommends course-relevant tasks to learners as drawn from
online freelance platforms. Rec-$ys has been deployed into a data analysis MOOC and is currently under evaluation.
Original languageEnglish
Title of host publicationLAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages578-579
Number of pages2
ISBN (Electronic)978-1-4503-4870-6
DOIs
StatePublished - 2017
EventLAK 2017 - Vancouver, BC, Canada
Duration: 13 Mar 201717 Mar 2017
Conference number: 7
http://lak17.solaresearch.org/

Conference

ConferenceLAK 2017
Abbreviated titleLAK'17
CountryCanada
CityVancouver, BC
Period13/03/1717/03/17
Internet address

    Research areas

  • Learning Analytics, Learning Design, MOOCs

ID: 33898476