"twitter archeology" of learning analytics and knowledge conferences

Bodong Chen, Xin Chen, Wanli Xing

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

9 Scopus citations

Abstract

The goal of the present study was to uncover new insights about the learning analytics community by analyzing Twitter archives from the past four Learning Analytics and Knowledge (LAK) conferences. Through descriptive analysis, in- teraction network analysis, hashtag analysis, and topic modeling, we found: extended coverage of the community over the years; increasing interactions among its members regard- less of peripheral and in-persistent participation; increasingly dense, connected and balanced social networks; and more and more diverse research topics. Detailed inspection of semantic topics uncovered insights complementary to the analysis of LAK publications in previous research.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015
PublisherAssociation for Computing Machinery
Pages340-349
Number of pages10
ISBN (Electronic)9781450334174
DOIs
StatePublished - Mar 16 2015
Event5th International Conference on Learning Analytics and Knowledge, LAK 2015 - Poughkeepsie, United States
Duration: Mar 16 2015Mar 20 2015

Publication series

NameACM International Conference Proceeding Series
Volume16-20-March-2015

Other

Other5th International Conference on Learning Analytics and Knowledge, LAK 2015
CountryUnited States
CityPoughkeepsie
Period3/16/153/20/15

Keywords

  • Hashtag Analysis
  • Learning Analytics
  • Social Net-work
  • Topic Modeling
  • Twitter
  • Twitter Analytics

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