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

Guanliang Chen; D.J. Davis; Markus Krause; Claudia Hauff; Geert-Jan Houben

doi:10.1145/3027385.3029469

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

Guanliang Chen, D.J. Davis, Markus Krause, Claudia Hauff, Geert-Jan Houben

Web Information Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

60 Downloads (Pure)

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.

Original language	English
Title of host publication	LAK 2017 Conference Proceedings of the 7th International Learning Analytics and Knowledge Conference
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	578-579
Number of pages	2
ISBN (Electronic)	978-1-4503-4870-6
DOIs	https://doi.org/10.1145/3027385.3029469
Publication status	Published - 2017
Event	LAK 2017: 7th International Conference on Learning Analytics and Knowledge - Vancouver, BC, Canada Duration: 13 Mar 2017 → 17 Mar 2017 Conference number: 7 http://lak17.solaresearch.org/

Conference

Conference	LAK 2017
Abbreviated title	LAK'17
Country/Territory	Canada
City	Vancouver, BC
Period	13/03/17 → 17/03/17
Internet address	http://lak17.solaresearch.org/

Keywords

Learning Analytics
Learning Design
MOOCs

Access to Document

10.1145/3027385.3029469

3389476Final published version, 233 KBLicence: CC BY-NC-ND

http://dl.acm.org/citation.cfm?id=3029469

Cite this

@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",

note = "LAK 2017 : 7th International Conference on Learning Analytics and Knowledge, LAK'17 ; Conference date: 13-03-2017 Through 17-03-2017",

url = "http://lak17.solaresearch.org/",

}

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

Buying time: Enabling learners to become earners with a real-world paid task recommender system. / Chen, Guanliang; Davis, D.J.; Krause, Markus et al.
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/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Buying time

T2 - LAK 2017

AU - Chen, Guanliang

AU - Davis, D.J.

AU - Krause, Markus

AU - Hauff, Claudia

AU - Houben, Geert-Jan

N1 - Conference code: 7

PY - 2017

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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

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DO - 10.1145/3027385.3029469

M3 - Conference contribution

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

PB - Association for Computing Machinery (ACM)

CY - New York, NY

Y2 - 13 March 2017 through 17 March 2017

ER -

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

Abstract

Conference

Keywords

Access to Document

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