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 learn-ing 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: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge |
Publisher | Association for Computing Machinery (ACM) |
Pages | 230-239 |
Number of pages | 10 |
ISBN (Print) | 9781450377126 |
DOIs | |
Publication status | Published - 23 Mar 2020 |
Event | The 10th International Learning Analytics & Knowledge Conference - Frankfurt, Germany Duration: 23 Mar 2020 → 27 Mar 2020 Conference number: 10 https://lak20.solaresearch.org/ |
Conference
Conference | The 10th International Learning Analytics & Knowledge Conference |
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Abbreviated title | LAK20 |
Country/Territory | Germany |
City | Frankfurt |
Period | 23/03/20 → 27/03/20 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Learning analytics
- privacy
- expectations
- privacy paradox
- higher education