The privacy paradox and its implications for learning analytics

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12 Citations (Scopus)

Abstract

Learning analytics promises to support adaptive learning in higher education. However, the associated issues around privacy protection, especially their implications for students as data subjects, has been a hurdle to wide-scale adoption. In light of this, we set out to understand student expectations of privacy issues related to learning analytics and to identify gaps between what students desire and what they expect to happen or choose to do in reality when it comes to privacy protection. To this end, an investigation was carried out in a UK higher education institution using a survey (N=674) and six focus groups (26 students). The study highlight a number of key implications for learning analytics research and practice: (1) purpose, access, and anonymity are key benchmarks of ethics and privacy integrity; (2) transparency and communication are key levers for learning analytics adoption; and (3) information asymmetry can impede active participation of students in learning analytics.

Original languageEnglish
Title of host publicationLAK'20
Subtitle of host publicationProceedings of the Tenth International Conference on Learning Analytics & Knowledge
EditorsMaren Scheffel, Vitomir Kovanović, Niels Pinkwart, Katrien Verbert
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages230-239
Number of pages10
ISBN (Electronic)9781450377126
DOIs
Publication statusPublished - 2020
EventInternational Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10th
https://lak20.solaresearch.org (Website)
https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3375462 (Website)

Conference

ConferenceInternational Conference on Learning Analytics and Knowledge 2020
Abbreviated titleLAK 2020
Country/TerritoryGermany
CityFrankfurt
Period23/03/2027/03/20
Internet address

Keywords

  • Expectations
  • Higher education
  • Learning analytics
  • Privacy
  • Privacy paradox

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