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Tools for predicting drop-off in large online classes

Authors:

Justin Cheng,

Chinmay Kulkarni,

Scott KlemmerAuthors Info & Claims

CSCW '13: Proceedings of the 2013 conference on Computer supported cooperative work companion

Pages 121 - 124

https://doi.org/10.1145/2441955.2441987

Published: 23 February 2013 Publication History

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Abstract

This paper describes two diagnostic tools to predict students are at risk of dropping out from an online class. While thousands of students have been attracted to large online classes, keeping them motivated has been challenging. Experiments on a large, online HCI class suggest that the tools these paper introduces can help identify students who will not complete assignments, with an F₁ score of 0.46 and 0.73 three days before the assignment due date.

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(2019)Next learning topic prediction for learner's guidance in informal learning environmentInternational Journal of Technology Enhanced Learning10.5555/3302810.330281411:1(62-70)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3302810.3302814
Caraban AKarapanos EGonçalves DCampos PBrewster SFitzpatrick GCox AKostakos V(2019)23 Ways to NudgeProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300733(1-15)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300733
Soffer TCohen A(2019)Students' engagement characteristics predict success and completion of online coursesJournal of Computer Assisted Learning10.1111/jcal.1234035:3(378-389)Online publication date: 17-Jan-2019
https://doi.org/10.1111/jcal.12340
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Index Terms

Tools for predicting drop-off in large online classes
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

CSCW '13: Proceedings of the 2013 conference on Computer supported cooperative work companion

February 2013

356 pages

ISBN:9781450313322

DOI:10.1145/2441955

General Chairs:
Amy Bruckman
Georgia Institute of Technology, USA
,
Scott Counts
Microsoft Research, USA
,
Program Chairs:
Cliff Lampe
University of Michigan, USA
,
Loren Terveen
University of Minnesota, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 February 2013

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CSCW '13

Sponsor:

SIGCHI

CSCW '13: Computer Supported Cooperative Work

February 23 - 27, 2013

Texas, San Antonio, USA

Acceptance Rates

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

Upcoming Conference

CSCW '25

Sponsor:
sigchi

Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

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Cited By

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(2019)Next learning topic prediction for learner's guidance in informal learning environmentInternational Journal of Technology Enhanced Learning10.5555/3302810.330281411:1(62-70)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3302810.3302814
Caraban AKarapanos EGonçalves DCampos PBrewster SFitzpatrick GCox AKostakos V(2019)23 Ways to NudgeProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300733(1-15)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300733
Soffer TCohen A(2019)Students' engagement characteristics predict success and completion of online coursesJournal of Computer Assisted Learning10.1111/jcal.1234035:3(378-389)Online publication date: 17-Jan-2019
https://doi.org/10.1111/jcal.12340
Glick DCohen AFestinger EXu DLi QWarschauer M(2019)Predicting Success, Preventing FailureUtilizing Learning Analytics to Support Study Success10.1007/978-3-319-64792-0_14(249-273)Online publication date: 18-Jan-2019
https://doi.org/10.1007/978-3-319-64792-0_14
Gardner JBrooks C(2018)Student success prediction in MOOCsUser Modeling and User-Adapted Interaction10.1007/s11257-018-9203-z28:2(127-203)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11257-018-9203-z
Korosi GHavasi F(2017)Moodle-based data mining potentials of MOOC systems at the University of Szeged2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)10.23919/MIPRO.2017.7973523(755-760)Online publication date: May-2017
https://doi.org/10.23919/MIPRO.2017.7973523
Kow YChen YCheng WNit SShamma DYew JBailey B(2017)Creative Information Use Among Young AdultsProceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition10.1145/3059454.3059473(407-420)Online publication date: 22-Jun-2017
https://dl.acm.org/doi/10.1145/3059454.3059473
Hong BWei ZYang Y(2017)Online education performance prediction via time-related features2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)10.1109/ICIS.2017.7959976(95-100)Online publication date: May-2017
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Cohen A(2017)Analysis of student activity in web-supported courses as a tool for predicting dropoutEducational Technology Research and Development10.1007/s11423-017-9524-365:5(1285-1304)Online publication date: 14-Apr-2017
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de Castro e Lima Baesse DGrisolia Ade Oliveira A(2016)Pedagogical monitoring as a tool to reduce dropout in distance learning in family healthBMC Medical Education10.1186/s12909-016-0735-916:1Online publication date: 22-Aug-2016
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