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

Guanliang Chen, Dan Davis, Markus Krause, Claudia Hauff, Geert-Jan Houben

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

1 Citation (Scopus)


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
Subtitle of host publicationThe Seventh International Learning Analytics & Knowledge Conference
EditorsInge Molenaar, Xavier Ochoa, Shane Dawson
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Electronic)9781450348706
Publication statusPublished - 2017
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2017 - Morris J Wosk Centre for Dialogue, Simon Fraser University, Vancouver, Canada
Duration: 13 Mar 201717 Mar 2017
Conference number: 7th


ConferenceInternational Learning Analytics & Knowledge Conference 2017
Abbreviated titleLAK 2017
Internet address


  • Learning analytics
  • Learning design
  • MOOCs

Cite this