Towards a data archiving solution for learning analytics

Sarah Taylor, Pablo Munguia

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

3 Citations (Scopus)

Abstract

Data solutions in the teaching and learning space are in need of pro-active innovations in data management, to ensure that systems for learning analytics can scale up to match the size of datasets now available. Here, we illustrate the scale at which a Learning Management System (LMS) accumulates data, and discuss the barriers to using this data for in-depth analyses. We illustrate the exponential growth of our LMS data to represent a single example dataset, and highlight the broader need for taking a pro-active approach to dimensional modelling in learning analytics, anticipating that common learning analytics questions will be computationally expensive, and that the most useful data structures for learning analytics will not necessarily follow those of the source dataset.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Learning Analytics and Knowledge
Subtitle of host publicationTowards User-Centred Learning Analytics, LAK 2018
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages260-264
Number of pages5
ISBN (Electronic)9781450364003
DOIs
Publication statusPublished - 7 Mar 2018
Externally publishedYes
Event8th International Conference on Learning Analytics and Knowledge, LAK 2018 - Sydney, Australia
Duration: 5 Mar 20189 Mar 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference8th International Conference on Learning Analytics and Knowledge, LAK 2018
Country/TerritoryAustralia
CitySydney
Period5/03/189/03/18

Keywords

  • Barriers to adoption
  • Big data
  • Data retention
  • Dimensional modelling
  • Learning analytics
  • Learning management systems

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