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Buying time : Enabling learners to become earners with a real-world paid task recommender system. / Chen, Guanliang; Davis, D.J.; Krause, Markus; Hauff, Claudia; Houben, Geert-Jan.

LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference. New York, NY : Association for Computing Machinery (ACM), 2017. p. 578-579.

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

Harvard

Chen, G, Davis, DJ, Krause, M, Hauff, C & Houben, G-J 2017, Buying time: Enabling learners to become earners with a real-world paid task recommender system. in LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference. Association for Computing Machinery (ACM), New York, NY, pp. 578-579, LAK 2017, Vancouver, BC, Canada, 13/03/17. https://doi.org/10.1145/3027385.3029469

APA

Chen, G., Davis, D. J., Krause, M., Hauff, C., & Houben, G-J. (2017). Buying time: Enabling learners to become earners with a real-world paid task recommender system. In LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference (pp. 578-579). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3027385.3029469

Vancouver

Chen G, Davis DJ, Krause M, Hauff C, Houben G-J. Buying time: Enabling learners to become earners with a real-world paid task recommender system. In LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference. New York, NY: Association for Computing Machinery (ACM). 2017. p. 578-579 https://doi.org/10.1145/3027385.3029469

Author

Chen, Guanliang ; Davis, D.J. ; Krause, Markus ; Hauff, Claudia ; Houben, Geert-Jan. / Buying time : Enabling learners to become earners with a real-world paid task recommender system. LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference. New York, NY : Association for Computing Machinery (ACM), 2017. pp. 578-579

BibTeX

@inproceedings{f100efd4638b4939bec8670af80020aa,
title = "Buying time: Enabling learners to become earners with a real-world paid task recommender system",
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.",
keywords = "Learning Analytics, Learning Design, MOOCs",
author = "Guanliang Chen and D.J. Davis and Markus Krause and Claudia Hauff and Geert-Jan Houben",
year = "2017",
doi = "10.1145/3027385.3029469",
language = "English",
pages = "578--579",
booktitle = "LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Buying time

T2 - Enabling learners to become earners with a real-world paid task recommender system

AU - Chen, Guanliang

AU - Davis, D.J.

AU - Krause, Markus

AU - Hauff, Claudia

AU - Houben, Geert-Jan

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Learning Analytics

KW - Learning Design

KW - MOOCs

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

U2 - 10.1145/3027385.3029469

DO - 10.1145/3027385.3029469

M3 - Conference contribution

SP - 578

EP - 579

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

PB - Association for Computing Machinery (ACM)

CY - New York, NY

ER -

ID: 33898476