Writing analytics literacy - Bridging from research to practice

Simon Knight, Laura Allen, Andrew Gibson, Danielle McNamara, Simon Buckingham Shum

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

2 Scopus citations

Abstract

There is untapped potential in achieving the full impact of learning analytics through the integration of tools into practical pedagogic contexts. To meet this potential, more work must be conducted to support educators in developing learning analytics literacy. The proposed workshop addresses this need by building capacity in the learning analytics community and developing an approach to resourcing for building 'writing analytics literacy'.

Original languageEnglish (US)
Title of host publicationLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publicationUnderstanding, Informing and Improving Learning with Data
PublisherAssociation for Computing Machinery
Pages496-497
Number of pages2
ISBN (Electronic)9781450348706
DOIs
StatePublished - Mar 13 2017
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Publication series

NameACM International Conference Proceeding Series

Other

Other7th International Conference on Learning Analytics and Knowledge, LAK 2017
CountryCanada
CityVancouver
Period3/13/173/17/17

Keywords

  • Analytics for action
  • Automated writing evaluation
  • Learning analytics
  • Learning analytics literacy
  • Practitioner knowledge
  • Writing analytics

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Knight, S., Allen, L., Gibson, A., McNamara, D., & Shum, S. B. (2017). Writing analytics literacy - Bridging from research to practice. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 496-497). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3027385.3029425