Buying time
Enabling learners to become earners with a real-world paid task recommender system
Guanliang Chen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
D.J. Davis (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Markus Krause (University of California)
Claudia Hauff (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Geert-Jan Houben (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
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.
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