poster

Visual analytics of MOOCs at maryland

Authors:

Zhengzheng Xu,

Dan Goldwasser,

Benjamin B. Bederson,

Jimmy LinAuthors Info & Claims

L@S '14: Proceedings of the first ACM conference on Learning @ scale conference

Pages 195 - 196

https://doi.org/10.1145/2556325.2567878

Published: 04 March 2014 Publication History

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Abstract

We use visual analytics to explore participation in five MOOCs at the University of Maryland. In some of these courses, our analysis reveals interesting clustering patterns of student behavior. For other courses, visualizations provide "color" to help us better understand the range of student behavior.

References

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Jordan, K. MOOC Completion Rates: The Data {Web-site}. (Accessed Jan 2, 2014) http://www.katyjordan.com/MOOCproject.html

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Kizilcec, R. F., Piech, C., and Schneider, E. Decon-structing disengagement: analyzing learner subpopula-tions in massive open online courses. In LAK '13, ACM (2013), 170--179. http://dl.acm.org/citation.cfm?doid=2460296.2460330

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Seaton, D. T., Bergner, Y., Chuang, I., Mitros, P., and Pritchard, D. E. Who does what in a massive open online course? Communications of the ACM, to appear (2014).

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Cited By

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Mubarak AAhmed SCao H(2021)MOOC-ASV: analytical statistical visual model of learners’ interaction in videos of MOOC coursesInteractive Learning Environments10.1080/10494820.2021.191676831:5(3055-3070)Online publication date: 25-Apr-2021
https://doi.org/10.1080/10494820.2021.1916768
Drivas IGiannakopoulos GSakas D(2020)Learning Analytics in Big Data Era. Exploration, Validation and Predictive Models DevelopmentIntelligent Tutoring Systems10.1007/978-3-030-49663-0_50(407-410)Online publication date: 3-Jun-2020
https://doi.org/10.1007/978-3-030-49663-0_50
Mubarak ACao HZhang WZhang W(2020)Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' coursesComputer Applications in Engineering Education10.1002/cae.2232829:4(710-732)Online publication date: 9-Sep-2020
https://doi.org/10.1002/cae.22328
Show More Cited By

Index Terms

Visual analytics of MOOCs at maryland
1. Applied computing
  1. Education
    1. Distance learning
    2. E-learning

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Published In

L@S '14: Proceedings of the first ACM conference on Learning @ scale conference

March 2014

234 pages

ISBN:9781450326698

DOI:10.1145/2556325

General Chair:
Mehran Sahami
Stanford University, USA
,
Program Chairs:
Armando Fox
University of California, Berkeley, USA
,
Marti A. Hearst
University of California, Berkeley, USA
,
Michelene T.H. Chi
Arizona State University, USA

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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2014

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L@S 2014

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ACM Ed Board

L@S 2014: First (2014) ACM Conference on Learning @ Scale

March 4 - 5, 2014

Georgia, Atlanta, USA

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L@S '14 Paper Acceptance Rate 14 of 38 submissions, 37%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Mubarak AAhmed SCao H(2021)MOOC-ASV: analytical statistical visual model of learners’ interaction in videos of MOOC coursesInteractive Learning Environments10.1080/10494820.2021.191676831:5(3055-3070)Online publication date: 25-Apr-2021
https://doi.org/10.1080/10494820.2021.1916768
Drivas IGiannakopoulos GSakas D(2020)Learning Analytics in Big Data Era. Exploration, Validation and Predictive Models DevelopmentIntelligent Tutoring Systems10.1007/978-3-030-49663-0_50(407-410)Online publication date: 3-Jun-2020
https://doi.org/10.1007/978-3-030-49663-0_50
Mubarak ACao HZhang WZhang W(2020)Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' coursesComputer Applications in Engineering Education10.1002/cae.2232829:4(710-732)Online publication date: 9-Sep-2020
https://doi.org/10.1002/cae.22328
Zhang JDu JXu F(2018)Application of Data Mining in MOOCs for Developing Vocational Education: A Review and Future Research DirectionsInternational Journal of Information and Education Technology10.18178/ijiet.2018.8.6.10738:6(411-417)Online publication date: 2018
https://doi.org/10.18178/ijiet.2018.8.6.1073
Chen QChen YLiu DShi CWu YQu H(2016)PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online CoursesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2015.250530522:10(2315-2330)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1109/TVCG.2015.2505305
Yulianto BPrabowo HKosala RHapsara M(2016)MOOC architecture model for computer programming courses2016 International Conference on Information Management and Technology (ICIMTech)10.1109/ICIMTech.2016.7930298(35-40)Online publication date: Nov-2016
https://doi.org/10.1109/ICIMTech.2016.7930298
Baneres DCaballe SClariso R(2016)Towards a Learning Analytics Support for Intelligent Tutoring Systems on MOOC Platforms2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)10.1109/CISIS.2016.48(103-110)Online publication date: Jul-2016
https://doi.org/10.1109/CISIS.2016.48
Pardos ZWhyte AKao K(2016)moocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and VisualizationTechnology, Knowledge and Learning10.1007/s10758-015-9268-221:1(75-98)Online publication date: 22-Jan-2016
https://doi.org/10.1007/s10758-015-9268-2
Pardos ZKao KKiczales GRussell DWoolf B(2015)moocRPProceedings of the Second (2015) ACM Conference on Learning @ Scale10.1145/2724660.2724683(103-110)Online publication date: 14-Mar-2015
https://dl.acm.org/doi/10.1145/2724660.2724683

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MOOCs: Is There an App for That? Expanding Mobilegogy through an Analysis of MOOCS and iTunes University

Retention in MOOCs: Understanding Users' Motivations, Perceptions and Activity Trajectories

Using learning analytics to explore help-seeking learner profiles in MOOCs

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