Print Email Facebook Twitter 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 clicksaway, 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-$ys), which automatically recommends course-relevant tasks to learners as drawn fromonline freelance platforms. Rec-$ys has been deployed into a data analysis MOOC and is currently under evaluation. Subject Learning AnalyticsLearning DesignMOOCs 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 Event LAK 2017, 2017-03-13 → 2017-03-17, Vancouver, BC, Canada Part of collection Institutional Repository Document type conference paper Rights © 2017 G. Chen, D.J. Davis, Markus Krause, C. Hauff, G.J.P.M. Houben Files PDF 3389476.pdf 322.83 KB Close viewer /islandora/object/uuid:f100efd4-638b-4939-bec8-670af80020aa/datastream/OBJ/view