Visualizing semantic space of online discourse: The Knowledge Forum case

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This poster presents an early experimentation of applying topic modeling and visualization techniques to analyze on- line 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.

Original languageEnglish (US)
Title of host publicationLAK 2014
Subtitle of host publication4th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages271-272
Number of pages2
ISBN (Print)1595930361, 9781595930361
DOIs
StatePublished - Jan 1 2014
Event4th International Conference on Learning Analytics and Knowledge, LAK 2014 - Indianapolis, IN, United States
Duration: Mar 24 2014Mar 28 2014

Publication series

NameACM International Conference Proceeding Series

Other

Other4th International Conference on Learning Analytics and Knowledge, LAK 2014
CountryUnited States
CityIndianapolis, IN
Period3/24/143/28/14

Keywords

  • Collaborative learning
  • Discourse analysis
  • Knowledge building
  • LDA
  • Semantic analysis
  • Text mining

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