Sepp, Stoo
In: Journal of Learning Analytics, Bd. 12, Nr. 3, S. 34–46, 2025, ISSN: 1929-7750.
Abstract | Links | BibTeX | Schlagwörter: analytics practice, disclosure, ethics, O, Privacy, research paper, transparency
@article{Sepp2025,
title = {Towards More Transparency in Learning Analytics: Sharing Information with University Students Increases their Awareness of Data Collection Practices},
author = {Stoo Sepp},
url = {https://doi.org/10.18608/jla.2025.8713},
doi = {10.18608/jla.2025.8713},
issn = {1929-7750},
year = {2025},
date = {2025-12-15},
urldate = {2025-12-15},
journal = {Journal of Learning Analytics},
volume = {12},
number = {3},
pages = {34–46},
publisher = {Society for Learning Analytics Research},
abstract = {As learning analytics practices become more commonplace in educational settings, student knowledge about the collection and use of their data becomes more of an interest. How students perceive the collection and use of their data has been researched for many years, with legitimate privacy and ethical concerns raised. While various guidelines, models, and frameworks have been proposed to address these concerns, the ways educational institutions practically address them by providing more information for increased transparency has yet to be widely investigated. The present study provides an initial investigation into the effectiveness of three different formats of data disclosure statements in a higher education setting. Participants were presented with one of three different formats from a fictional university: 1) a generic text, 2) a detailed text, or 3) an icon-based “nutrition label.” Participants then completed a survey to assess their understanding and perceptions of data collection practices. Results suggest that regardless of format, participants demonstrated an increased understanding of these practices when the disclosure was not generic. Additionally, student acceptance of data collection and beliefs about sharing were unaffected by disclosure of any kind. This study provides initial evidence to inform learning analytics practices to address identified concerns from students around a lack of transparency. Universities and other institutions in the higher education sector may revisit their data disclosure methods and language to ensure that they are both accurate and transparent, so that students better understand data collection within the scope of their studies.},
keywords = {analytics practice, disclosure, ethics, O, Privacy, research paper, transparency},
pubstate = {published},
tppubtype = {article}
}
As learning analytics practices become more commonplace in educational settings, student knowledge about the collection and use of their data becomes more of an interest. How students perceive the collection and use of their data has been researched for many years, with legitimate privacy and ethical concerns raised. While various guidelines, models, and frameworks have been proposed to address these concerns, the ways educational institutions practically address them by providing more information for increased transparency has yet to be widely investigated. The present study provides an initial investigation into the effectiveness of three different formats of data disclosure statements in a higher education setting. Participants were presented with one of three different formats from a fictional university: 1) a generic text, 2) a detailed text, or 3) an icon-based “nutrition label.” Participants then completed a survey to assess their understanding and perceptions of data collection practices. Results suggest that regardless of format, participants demonstrated an increased understanding of these practices when the disclosure was not generic. Additionally, student acceptance of data collection and beliefs about sharing were unaffected by disclosure of any kind. This study provides initial evidence to inform learning analytics practices to address identified concerns from students around a lack of transparency. Universities and other institutions in the higher education sector may revisit their data disclosure methods and language to ensure that they are both accurate and transparent, so that students better understand data collection within the scope of their studies.
Krüger, Nicolai; Stibe, Agnis; Teuteberg, Frank
The Black Mirror: What Your Mobile Phone Number Reveals About You. Konferenzberichte
Springer, Cham, Bd. 389, 2020, ISBN: 978-3-030-53337-3.
Abstract | Links | BibTeX | Schlagwörter: A, Information privacy, Mobile Device Management, Mobile phone privacy, Privacy, Privacy scoring model, Social media privacy
@proceedings{Krüger2020,
title = {The Black Mirror: What Your Mobile Phone Number Reveals About You.},
author = {Nicolai Krüger and Agnis Stibe and Frank Teuteberg},
editor = {Witold Abramowicz and Gary Klein},
url = {https://doi.org/10.1007/978-3-030-53337-3_2},
doi = {10.1007/978-3-030-53337-3_2},
isbn = {978-3-030-53337-3},
year = {2020},
date = {2020-06-08},
urldate = {2020-11-04},
booktitle = {Business Information Systems, 23rd International Conference, BIS 2020, Colorado Springs, CO, USA},
volume = {389},
pages = {18–32},
publisher = {Springer},
address = {Cham},
series = {Lecture Notes in Business Information Processing},
abstract = {In the present era of pervasive mobile technologies, interconnecting innovations are increasingly prevalent in our lives. In this evolutionary process, mobile and social media communication systems serve as a backbone for human interactions. When assessing privacy risks related to this, privacy scoring models (PSM) can help quantifying the personal information risks. This paper uses the mobile phone number itself as a basis for privacy scoring. We tested 1,000 random phone numbers for their matching to social media accounts. The results raise concerns how network and communication layers are predominately connected. PSMs will support future organizational sensitivity for data linkability.},
keywords = {A, Information privacy, Mobile Device Management, Mobile phone privacy, Privacy, Privacy scoring model, Social media privacy},
pubstate = {published},
tppubtype = {proceedings}
}
In the present era of pervasive mobile technologies, interconnecting innovations are increasingly prevalent in our lives. In this evolutionary process, mobile and social media communication systems serve as a backbone for human interactions. When assessing privacy risks related to this, privacy scoring models (PSM) can help quantifying the personal information risks. This paper uses the mobile phone number itself as a basis for privacy scoring. We tested 1,000 random phone numbers for their matching to social media accounts. The results raise concerns how network and communication layers are predominately connected. PSMs will support future organizational sensitivity for data linkability.