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

Chen, G.; Davis, D.J.; Krause, Markus; Hauff, C.; Houben, G.J.P.M.

doi:10.1145/3027385.3029469

Buying time

Title

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

Author

Chen, G. (TU Delft Web Information Systems)
Davis, D.J. (TU Delft Web Information Systems)
Krause, Markus (University of California)
Hauff, C. (TU Delft Web Information Systems)
Houben, G.J.P.M. (TU Delft Web Information Systems)

Date

2017

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.

Subject

Learning Analytics
Learning Design
MOOCs

To reference this document use:

http://resolver.tudelft.nl/uuid:f100efd4-638b-4939-bec8-670af80020aa

DOI

https://doi.org/10.1145/3027385.3029469

Publisher

Association for Computing Machinery (ACM), New York, NY

ISBN

978-1-4503-4870-6

Source

LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference