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Visualizing semantic space of online discourse: the knowledge forum case

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Bodong ChenAuthors Info & Claims

LAK '14: Proceedings of the Fourth International Conference on Learning Analytics And Knowledge

Pages 271 - 272

https://doi.org/10.1145/2567574.2567595

Published: 24 March 2014 Publication History

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Abstract

This poster presents an early experimentation of applying topic modeling and visualization techniques to analyze online discourse. In particular, Latent Dirichlet Allocation was used to convert discourse into a high-dimensional semantic space. To explore meaningful visualizations of the space, Locally Linear Embedding was performed reducing it to two-dimensional. Further, Time Series Analysis was applied to track evolution of topics in the space. This work will lead to new analytic tools for collaborative learning.

References

[1]

A. Bakharia and S. Dawson. SNAPP. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge - LAK '11, page 168, New York, New York, USA, 2011. ACM Press.

Digital Library

Google Scholar

[2]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 3:993--1022, Mar. 2003.

Digital Library

Google Scholar

[3]

G. Dyke, R. Kumar, H. Ai, and C. Rosé. Challenging assumptions: Using sliding window visualizations to reveal time-based irregularities in CSCL processes. In J. van Aalst, K. Thompson, M. J. Jacobson, and P. Reimann, editors, The future of learning: Proceedings of the 10th international conference of the learning sciences (ICLS 2012) - Volume 1, Full Papers, pages 363--370. ISLS, Sydney, Australia, 2012.

Google Scholar

[4]

J. Oshima, R. Oshima, and Y. Matsuzawa. Knowledge Building Discourse Explorer: a social network analysis application for knowledge building discourse. Educational Technology Research and Development, June 2012.

Crossref

Google Scholar

[5]

C. Romero and S. Ventura. Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1):135--146, July 2007.

Digital Library

Google Scholar

[6]

S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science (New York, N.Y.), 290(5500):2323--6, Dec. 2000.

Crossref

Google Scholar

[7]

M. Scardamalia. CSILE/Knowledge Forum. In A. Kovalchick and K. Dawson, editors, Education and technology: An encyclopedia, pages 183--192. ABC-CLIO, Santa Barbara, CA, 2004.

Google Scholar

[8]

M. Scardamalia and C. Bereiter. Knowledge building. In J. W. Guthrie, editor, Encyclopedia of education, volume 17, pages 1370--1373. Macmillan Reference, New York, NY, 2 edition, 2003.

Google Scholar

[9]

A.-H. Tan. Text mining: The state of the art and the challenges. In Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases, pages 65--70, 1999.

Google Scholar

[10]

C. Teplovs, Z. Donoahue, M. Scardamalia, and D. Philip. Tools for Concurrent, Embedded, and Transformative Assessment of Knowledge Building Processes and Progress. In Proceedings of the 8th iternational conference on Computer supported collaborative learning, pages 721--723, New Brunswick, New Jersey, USA, 2007. International Society of the Learning Sciences.

Digital Library

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Xing WLee HShibani A(2020)Identifying patterns in students’ scientific argumentation: content analysis through text mining using Latent Dirichlet AllocationEducational Technology Research and Development10.1007/s11423-020-09761-w68:5(2185-2214)Online publication date: 16-Mar-2020
https://doi.org/10.1007/s11423-020-09761-w
Alabi H(2016)A Language and a SpaceDeveloping Effective Educational Experiences through Learning Analytics10.4018/978-1-4666-9983-0.ch001(1-41)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9983-0.ch001

Index Terms

Visualizing semantic space of online discourse: the knowledge forum case
1. Applied computing
  1. Education
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

LAK '14: Proceedings of the Fourth International Conference on Learning Analytics And Knowledge

March 2014

301 pages

ISBN:9781450326643

DOI:10.1145/2567574

Conference Chairs:
Mattew Pistilli
Purdue University, IN
,
James Willis
Purdue University, IN
,
Drew Koch
John N. Gardner Institute for Excellence in Undergraduate Education, NC
,
Kimberly Arnold
University of Wisconsin-Madison, WI
,
Program Chairs:
Stephanie Teasley
University of Michigan, MI
,
Abelardo Pardo
The University of Sydney, Australia

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

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Published: 24 March 2014

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LAK '14

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JNGI
University of Wisc-Madison
SoLAR
Purdue University

LAK '14: Learning Analytics and Knowledge Conference 2014

March 24 - 28, 2014

Indiana, Indianapolis, USA

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LAK '14 Paper Acceptance Rate 13 of 44 submissions, 30%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Xing WLee HShibani A(2020)Identifying patterns in students’ scientific argumentation: content analysis through text mining using Latent Dirichlet AllocationEducational Technology Research and Development10.1007/s11423-020-09761-w68:5(2185-2214)Online publication date: 16-Mar-2020
https://doi.org/10.1007/s11423-020-09761-w
Alabi H(2016)A Language and a SpaceDeveloping Effective Educational Experiences through Learning Analytics10.4018/978-1-4666-9983-0.ch001(1-41)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9983-0.ch001

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Modeling Student Discourse in Online Discussion Forums Using Semantic Similarity Based Topic Chains

New Sentiment Analysis Model Using LDA for Arabic Tweets

Conversations of virtual learning communities: discourse and survey analyses of moderated online synchronous discussions

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