CROWDLEARNING: Towards collaborative problem-posing at scale

Alireza Farasat; Alexander Nikolaev; Suzanne Miller; Rahul Gopalsamy

doi:10.1145/3051457.3053990

CROWDLEARNING: Towards collaborative problem-posing at scale

Alireza Farasat
, Alexander Nikolaev
, Suzanne Miller
, Rahul Gopalsamy

SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

This paper presents a new pedagogical paradigm "Crowdlearning", where students experience deeper learning through collaboratively creating learning materials for each other. Crowdlearning practice is envisioned to produce large "banks" of subject matter problems generated by students themselves, in a crowdsourced way, as the students learn new subjects; these problems can then serve as learning and assessment materials usable at scale. This paper overviews the motivation for the development of Crowdlearning as a teaching practice and the theoretical drivers behind it. The paper then reports on preliminary field studies and experiences suggesting that Crowdlearning has a solid potential for adoption in STEM.

Original language	English
Title of host publication	L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale
Publisher	Association for Computing Machinery, Inc
Pages	221-224
Number of pages	4
ISBN (Electronic)	9781450344500
DOIs	https://doi.org/10.1145/3051457.3053990
State	Published - Apr 12 2017
Event	4th Annual ACM Conference on Learning at Scale, L@S 2017 - Cambridge, United States Duration: Apr 20 2017 → Apr 21 2017

Publication series

Name	L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale

Conference

Conference	4th Annual ACM Conference on Learning at Scale, L@S 2017
Country/Territory	United States
City	Cambridge
Period	04/20/17 → 04/21/17

Keywords

Collaborative learning
Crowdlearning
Intelligent tutoring systems
Learning technologies
Massive open online courses
Problem posing

Access to Document

10.1145/3051457.3053990

Cite this

Farasat, A., Nikolaev, A., Miller, S., & Gopalsamy, R. (2017). CROWDLEARNING: Towards collaborative problem-posing at scale. In L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale (pp. 221-224). (L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale). Association for Computing Machinery, Inc. https://doi.org/10.1145/3051457.3053990

@inproceedings{5751cc35c48d4435876cbfdc39f88b17,

title = "CROWDLEARNING: Towards collaborative problem-posing at scale",

abstract = "This paper presents a new pedagogical paradigm {"}Crowdlearning{"}, where students experience deeper learning through collaboratively creating learning materials for each other. Crowdlearning practice is envisioned to produce large {"}banks{"} of subject matter problems generated by students themselves, in a crowdsourced way, as the students learn new subjects; these problems can then serve as learning and assessment materials usable at scale. This paper overviews the motivation for the development of Crowdlearning as a teaching practice and the theoretical drivers behind it. The paper then reports on preliminary field studies and experiences suggesting that Crowdlearning has a solid potential for adoption in STEM.",

keywords = "Collaborative learning, Crowdlearning, Intelligent tutoring systems, Learning technologies, Massive open online courses, Problem posing",

author = "Alireza Farasat and Alexander Nikolaev and Suzanne Miller and Rahul Gopalsamy",

note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 4th Annual ACM Conference on Learning at Scale, L@S 2017 ; Conference date: 20-04-2017 Through 21-04-2017",

year = "2017",

month = apr,

day = "12",

doi = "10.1145/3051457.3053990",

language = "English",

series = "L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale",

publisher = "Association for Computing Machinery, Inc",

pages = "221--224",

booktitle = "L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale",

}

Farasat, A, Nikolaev, A , Miller, S & Gopalsamy, R 2017, CROWDLEARNING: Towards collaborative problem-posing at scale. in L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale. L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale, Association for Computing Machinery, Inc, pp. 221-224, 4th Annual ACM Conference on Learning at Scale, L@S 2017, Cambridge, United States, 04/20/17. https://doi.org/10.1145/3051457.3053990

CROWDLEARNING: Towards collaborative problem-posing at scale. / Farasat, Alireza; Nikolaev, Alexander ; Miller, Suzanne et al.
L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale. Association for Computing Machinery, Inc, 2017. p. 221-224 (L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CROWDLEARNING

T2 - 4th Annual ACM Conference on Learning at Scale, L@S 2017

AU - Farasat, Alireza

AU - Nikolaev, Alexander

AU - Miller, Suzanne

AU - Gopalsamy, Rahul

PY - 2017/4/12

Y1 - 2017/4/12

N2 - This paper presents a new pedagogical paradigm "Crowdlearning", where students experience deeper learning through collaboratively creating learning materials for each other. Crowdlearning practice is envisioned to produce large "banks" of subject matter problems generated by students themselves, in a crowdsourced way, as the students learn new subjects; these problems can then serve as learning and assessment materials usable at scale. This paper overviews the motivation for the development of Crowdlearning as a teaching practice and the theoretical drivers behind it. The paper then reports on preliminary field studies and experiences suggesting that Crowdlearning has a solid potential for adoption in STEM.

AB - This paper presents a new pedagogical paradigm "Crowdlearning", where students experience deeper learning through collaboratively creating learning materials for each other. Crowdlearning practice is envisioned to produce large "banks" of subject matter problems generated by students themselves, in a crowdsourced way, as the students learn new subjects; these problems can then serve as learning and assessment materials usable at scale. This paper overviews the motivation for the development of Crowdlearning as a teaching practice and the theoretical drivers behind it. The paper then reports on preliminary field studies and experiences suggesting that Crowdlearning has a solid potential for adoption in STEM.

KW - Collaborative learning

KW - Crowdlearning

KW - Intelligent tutoring systems

KW - Learning technologies

KW - Massive open online courses

KW - Problem posing

UR - https://www.scopus.com/pages/publications/85018381506

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DO - 10.1145/3051457.3053990

M3 - Conference contribution

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PB - Association for Computing Machinery, Inc

Y2 - 20 April 2017 through 21 April 2017

ER -

CROWDLEARNING: Towards collaborative problem-posing at scale

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Keywords

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