Buying time: Enabling learners to become earners with a real-world paid task recommender system

Guanliang Chen, D.J. Davis, Markus Krause, Claudia Hauff, Geert-Jan Houben

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
30 Downloads (Pure)


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)
Number of pages2
ISBN (Electronic)978-1-4503-4870-6
Publication statusPublished - 2017
EventLAK 2017: 7th International Conference on Learning Analytics and Knowledge - Vancouver, BC, Canada
Duration: 13 Mar 201717 Mar 2017
Conference number: 7


ConferenceLAK 2017
Abbreviated titleLAK'17
CityVancouver, BC
Internet address


  • Learning Analytics
  • Learning Design
  • MOOCs


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