Learning analytics and machine learning

Dragan Gasevic, Annika Wolff, Carolyn Rose, Zdenek Zdrahal, George Siemens

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

6 Citations (Scopus)

Abstract

Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.

Original languageEnglish
Title of host publicationLAK 2014
Subtitle of host publicationFourth International Conference on Learning Analytics and Knowledge
EditorsStephanie Teasley, Abelardo Pardo
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages287-288
Number of pages2
ISBN (Print)9781595930361, 9781450326643
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2014 - Indianapolis, United States of America
Duration: 24 Mar 201428 Mar 2014
Conference number: 4th
https://lak14indy.wordpress.com/

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2014
Abbreviated titleLAK 2014
CountryUnited States of America
CityIndianapolis
Period24/03/1428/03/14
Internet address

Keywords

  • Collaboration
  • Learning analytics
  • Machine learning
  • Theory

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