poster

Uncovering hidden engagement patterns for predicting learner performance in MOOCs

Authors:
Arti Ramesh

University of Maryland, College Park, College Park, MD, USA

University of Maryland, College Park, College Park, MD, USA
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,
Dan Goldwasser

University of Maryland, College Park, College Park, MD, USA

University of Maryland, College Park, College Park, MD, USA
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,
Bert Huang

University of Maryland, College Park, College Park, MD, USA

University of Maryland, College Park, College Park, MD, USA
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,
Hal Daume

University of Maryland, College Park, College Park, MD, USA

University of Maryland, College Park, College Park, MD, USA
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,
Lise Getoor

University of California, Santa Cruz, Santa Cruz, CA, USA

University of California, Santa Cruz, Santa Cruz, CA, USA
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L@S '14: Proceedings of the first ACM conference on Learning @ scale conferenceMarch 2014Pages 157–158https://doi.org/10.1145/2556325.2567857

Published:04 March 2014Publication History

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

Pages 157–158

ABSTRACT

Maintaining and cultivating student engagement is a prerequisite for MOOCs to have broad educational impact. Understanding student engagement as a course progresses helps characterize student learning patterns and can aid in minimizing dropout rates, initiating instructor intervention. In this paper, we construct a probabilistic model connecting student behavior and class performance, formulating student engagement types as latent variables. We show that our model identifies course success indicators that can be used by instructors to initiate interventions and assist students.

References

Broecheler,M., Mihalkova, L., and Getoor, L. Probabilistic similarity logic. In Uncertainty in Artificial Intelligence (UAI) (2010).Google Scholar
Ramesh, A., Goldwasser, D., Huang, B., Daume III, H., and Getoor, L. Modeling learner engagement in moocs using probabilistic soft logic. In NIPS Workshop on Data Driven Education (2013).Google Scholar
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., and Patwardhan, S. Opinionfinder: A system for subjectivity analysis. In Proceedings of HLT/EMNLP on Interactive Demonstrations (2005). Google ScholarDigital Library

Index Terms

Uncovering hidden engagement patterns for predicting learner performance in MOOCs
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
Copyright © 2014 Owner/Author
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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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
- Published: 4 March 2014
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Author Tags
learner engagement
mooc
probabilistic modeling
Qualifiers
- poster
Conference

Acceptance Rates
L@S '14 Paper Acceptance Rate14of38submissions,37%Overall Acceptance Rate117of440submissions,27%
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Uncovering hidden engagement patterns for predicting learner performance in MOOCs

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

ABSTRACT

References

Cited By

Index Terms

Recommendations

Visualizing patterns of student engagement and performance in MOOCs

Learning Engagement and Peer Learning in MOOC: A Selective Systematic Review

Learners Self-directing Learning in FutureLearn MOOCs: A Learner-Centered Study