research-article

Engaging with massive online courses

Authors:

Ashton Anderson,

Daniel Huttenlocher,

Jon Kleinberg,

Jure LeskovecAuthors Info & Claims

WWW '14: Proceedings of the 23rd international conference on World wide web

Pages 687 - 698

https://doi.org/10.1145/2566486.2568042

Published: 07 April 2014 Publication History

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Abstract

The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood.

In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course.

We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.

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Cited By

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Bao RLiang S(2024)The Feature and Diversity of Learners' Performance on Different MOOC Forums in the Online Learning EraInternational Journal of Distance Education Technologies10.4018/IJDET.35026722:1(1-21)Online publication date: 8-Aug-2024
https://doi.org/10.4018/IJDET.350267
Waladi CKhaldi MLamarti M(2024)Revolutionizing Education Through Personalized Pedagogy and Machine Learning AlgorithmsFostering Pedagogical Innovation Through Effective Instructional Design10.4018/979-8-3693-1206-3.ch016(342-362)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-1206-3.ch016
Wehrhahn FGaschler RZhao F(2024)Students Follow Structured Guidance in an Asynchronous Online-Only Course, Despite Diverging PreferencesPsychology Learning & Teaching10.1177/14757257241276078Online publication date: 24-Sep-2024
https://doi.org/10.1177/14757257241276078
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Index Terms

Engaging with massive online courses
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW '14: Proceedings of the 23rd international conference on World wide web

April 2014

926 pages

ISBN:9781450327442

DOI:10.1145/2566486

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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WWW '14 Paper Acceptance Rate 84 of 645 submissions, 13%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

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Bao RLiang S(2024)The Feature and Diversity of Learners' Performance on Different MOOC Forums in the Online Learning EraInternational Journal of Distance Education Technologies10.4018/IJDET.35026722:1(1-21)Online publication date: 8-Aug-2024
https://doi.org/10.4018/IJDET.350267
Waladi CKhaldi MLamarti M(2024)Revolutionizing Education Through Personalized Pedagogy and Machine Learning AlgorithmsFostering Pedagogical Innovation Through Effective Instructional Design10.4018/979-8-3693-1206-3.ch016(342-362)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-1206-3.ch016
Wehrhahn FGaschler RZhao F(2024)Students Follow Structured Guidance in an Asynchronous Online-Only Course, Despite Diverging PreferencesPsychology Learning & Teaching10.1177/14757257241276078Online publication date: 24-Sep-2024
https://doi.org/10.1177/14757257241276078
Zhu KKhern-am-nuai WYu Y(2024)Negative Peer Feedback and User Content Generation: Evidence From a Restaurant Review PlatformProduction and Operations Management10.1177/10591478231224941Online publication date: 8-Feb-2024
https://doi.org/10.1177/10591478231224941
Linhao ZShenghua ZZhijiao XGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Discovering Multi-Relational Integration for Knowledge Tracing with Retentive NetworksProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658030(960-968)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658030
Shen JQian HLiu SZhang WJiang BZhou ABaeza-Yates RBonchi F(2024)Capturing Homogeneous Influence among Students: Hypergraph Cognitive Diagnosis for Intelligent Education SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672002(2628-2639)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672002
Rivera DFrenay MPaquot M(2024) The role of MOOC forum discussion tasks in learners' cognitive engagement Journal of Computer Assisted Learning10.1111/jcal.1300140:5(2103-2120)Online publication date: 20-May-2024
https://doi.org/10.1111/jcal.13001
Wang LLi XLuo ZHu ZYan Q(2024)Multivariate Cognitive Response Framework for Student Performance Prediction on MOOCIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330284836:3(1221-1233)Online publication date: Mar-2024
https://doi.org/10.1109/TKDE.2023.3302848
Li JDeng YMao SQin YJiang Y(2024)Knowledge-Associated Embedding for Memory-Aware Knowledge TracingIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.330690911:3(4016-4028)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3306909
Salam HCeliktutan OGunes HChetouani M(2024)Automatic Context-Aware Inference of Engagement in HMI: A SurveyIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327870715:2(445-464)Online publication date: Apr-2024
https://doi.org/10.1109/TAFFC.2023.3278707
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