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 language | English |
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Title of host publication | LAK'20 |
Subtitle of host publication | Proceedings of the Tenth International Conference on Learning Analytics & Knowledge |
Editors | Maren Scheffel, Vitomir Kovanović, Niels Pinkwart, Katrien Verbert |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 230-239 |
Number of pages | 10 |
ISBN (Electronic) | 9781450377126 |
DOIs | |
Publication status | Published - 2020 |
Event | International Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany Duration: 23 Mar 2020 → 27 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
Conference | International Conference on Learning Analytics and Knowledge 2020 |
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Abbreviated title | LAK 2020 |
Country/Territory | Germany |
City | Frankfurt |
Period | 23/03/20 → 27/03/20 |
Internet address |
Keywords
- Expectations
- Higher education
- Learning analytics
- Privacy
- Privacy paradox