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Who You Are or What You Do: Comparing the Predictive Power of Demographics vs. Activity Patterns in Massive Open Online Courses (MOOCs)

Authors:

Christopher Brooks,

Craig Thompson,

Stephanie TeasleyAuthors Info & Claims

L@S '15: Proceedings of the Second (2015) ACM Conference on Learning @ Scale

Pages 245 - 248

https://doi.org/10.1145/2724660.2728668

Published: 14 March 2015 Publication History

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Abstract

Demographics factors have been used successfully as predictors of student success in traditional higher education systems, but their relationship to achievement in MOOC environments has been largely untested. In this work we explore the predictive power of user demographics compared to learner interaction trace data generated by students in two MOOCs. We show that demographic information offers minimal predictive power compared to activity models, even when compared to models created very early on in the course before substantial interaction data has accrued.

References

[1]

S. Jayaprakash, E. Moody, E. Lauría, J. Regan and J. Baron, "Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative," Journal of Learning Analytics, vol. 1, no. 1, 2014.

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Google Scholar

[2]

M. Sharkey, "Academic analytics landscape at the University of Phoenix," in 1st International Conference on Learning Analytics and Knowledge (LAK11), Banff, AB, 2011.

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Google Scholar

[3]

L. Macfadyen and S. Dawson, "Mining LMS data to develop an "early warning system" for educators: A proof of concept," Computers & Education, vol. 54, no. 2, pp. 588--599, 2010.

Digital Library

Google Scholar

[4]

C. Brooks, C. Thompson and S. Teasley, "A Time Series Interaction Analysis Method for Building Predictive Models of Learners using Log Data," in 5th International Conference on Learning Analytics and Knowledge 2015 (LAK15), Poughkeepsie, NY, 2015.

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Kleter SMatzat UConijn R(2024)Boundary Conditions of Generalizing Predictive Models for Academic Performance: Within Cohort Versus Within CourseIEEE Transactions on Learning Technologies10.1109/TLT.2024.344307917(2129-2140)Online publication date: 2024
https://doi.org/10.1109/TLT.2024.3443079
Salman AAlexandron G(2024)The Digital Fingerprint of Learner Behavior: Empirical Evidence for Individuality in Learning Using Deep LearningComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100322(100322)Online publication date: Oct-2024
https://doi.org/10.1016/j.caeai.2024.100322
Llanos JBucheli VRestrepo-Calle F(2023)Early prediction of student performance in CS1 programming coursesPeerJ Computer Science10.7717/peerj-cs.16559(e1655)Online publication date: 31-Oct-2023
https://doi.org/10.7717/peerj-cs.1655
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Index Terms

Who You Are or What You Do: Comparing the Predictive Power of Demographics vs. Activity Patterns in Massive Open Online Courses (MOOCs)
1. Applied computing
  1. Education

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Information & Contributors

Information

Published In

L@S '15: Proceedings of the Second (2015) ACM Conference on Learning @ Scale

March 2015

438 pages

ISBN:9781450334112

DOI:10.1145/2724660

General Chair:
Gregor Kiczales
University of British Columbia
,
Program Chairs:
Daniel M. Russell
Google
,
Beverly Woolf
University of Massachusets, Amherst

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2015

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Work in progress

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L@S 2015

Sponsor:

ACM Ed Board

L@S 2015: Second (2015) ACM Conference on Learning @ Scale

March 14 - 18, 2015

BC, Vancouver, Canada

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L@S '15 Paper Acceptance Rate 23 of 90 submissions, 26%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Kleter SMatzat UConijn R(2024)Boundary Conditions of Generalizing Predictive Models for Academic Performance: Within Cohort Versus Within CourseIEEE Transactions on Learning Technologies10.1109/TLT.2024.344307917(2129-2140)Online publication date: 2024
https://doi.org/10.1109/TLT.2024.3443079
Salman AAlexandron G(2024)The Digital Fingerprint of Learner Behavior: Empirical Evidence for Individuality in Learning Using Deep LearningComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100322(100322)Online publication date: Oct-2024
https://doi.org/10.1016/j.caeai.2024.100322
Llanos JBucheli VRestrepo-Calle F(2023)Early prediction of student performance in CS1 programming coursesPeerJ Computer Science10.7717/peerj-cs.16559(e1655)Online publication date: 31-Oct-2023
https://doi.org/10.7717/peerj-cs.1655
Utunen HTokar ADancante MPiroux C(2023)Online learning for WHO priority diseases with pandemic potential: evidence from existing courses and preparing for Disease XArchives of Public Health10.1186/s13690-023-01080-981:1Online publication date: 21-Apr-2023
https://doi.org/10.1186/s13690-023-01080-9
Tang SLei CWei H(2023) The effect of learning strategies adopted in K12 schools on student learning in massive open online courses Journal of Computer Assisted Learning10.1111/jcal.1293240:3(990-1005)Online publication date: 28-Dec-2023
https://doi.org/10.1111/jcal.12932
Shi HZhou Y(2023)Stay or Leave? Exploring Student Factors Associated with Dropout Patterns in Massive Open Online Courses2023 IEEE International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT58122.2023.00013(26-30)Online publication date: Jul-2023
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Eicher BJoyner DKizilcec RDavis KOchoa X(2022)An Examination of Unofficial Course Reviews in a Graduate Program at ScaleProceedings of the Ninth ACM Conference on Learning @ Scale10.1145/3491140.3528330(289-293)Online publication date: 1-Jun-2022
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Roy AYee MPerdue MStein JBell ACarter RMiyagawa SKizilcec RDavis KOchoa X(2022)How COVID-19 Affected Computer Science MOOC Learner Behavior and Achievements: A Demographic StudyProceedings of the Ninth ACM Conference on Learning @ Scale10.1145/3491140.3528328(345-349)Online publication date: 1-Jun-2022
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Fotso JBatchakui BNkambou ROkereke G(2022)Algorithms for the Development of Deep Learning Models for Classification and Prediction of Learner Behaviour in MOOCsArtificial Intelligence for Data Science in Theory and Practice10.1007/978-3-030-92245-0_3(41-73)Online publication date: 2022
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Bothe MSchwerer FMeinel C(2021)Video Consumption with Mobile Applications in a Global Enterprise MOOC ContextInnovations in Learning and Technology for the Workplace and Higher Education10.1007/978-3-030-90677-1_5(49-60)Online publication date: 13-Nov-2021
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