Student privacy self-management: implications for learning analytics
Abstract
Optimizing the harvesting and analysis of student data promises to clear the fog surrounding the key drivers of student success and retention, and provide potential for improved student success. At the same time, concerns are increasingly voiced around the extent to which individuals are routinely and progressively tracked as they engage online. The Internet, the very thing that promised to open up possibilities and to break down communication barriers, now threatens to narrow it again through the panopticon of mass surveillance.
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Nudging techniques direct students toward particular learning resources or to change their behaviors.
“So-called “nudging” techniques direct students toward particular learning resources or to change their behaviors.90”
From Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads (2018) · cites this paper as [90]
Prinsloo and Slade (2015) found several troubling gaps in MOOC providers' fiduciary duty to students and their consideration for privacy issues and resulting student vulnerability.
“In an analysis of the TACs of three providers of massive open online courses (MOOCs) namely Coursera, EdX, and FutureLearn, Prinsloo and Slade (2015) found several troubling gaps in both the providers' fiduciary duty to students and their consideration for the inherent privacy issues and resulting student vulnerability.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
It is increasingly possible that student data may be used inappropriately and unethically, further increasing the vulnerability of students.
“Though the intention of collecting and using student data arguably falls within the scope of the fiduciary duty of higher education, it is increasingly possible that student data may be also used inappropriately and unethically, further increasing the vulnerability of students (Prinsloo & Slade, 2015).”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
Individuals may adapt decisions about how much information they are prepared to share, and for what purpose, depending on the context.
“Individuals may adapt decisions regarding how much information they are prepared to share (and for what purpose) depending on the context.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
HEIs are increasingly vulnerable and must optimize their use of data, in particular student data.
“In this context, HEIs are increasingly vulnerable and must optimize their use of data, in particular student data (Baker & Siemens, 2014;Gašević & Siemens, 2015;Prinsloo & Slade, 2015).”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
Prinsloo and Slade identified six problems regarding informed consent: scale; re-identification and aggregation; quality, accountability, and purpose of data; drawbacks in setting limitations for use; impact of increasing openness and sharing; and problematics of user agency.
“Prinsloo and Slade (2015) identified the following problems regarding the notion of informed consent: 1) the problem of scale; 2) the re-identification and the problem of aggregation; 3) the quality, accountability, and purpose of data; 4) the drawbacks in setting limitations for use; 5) the impact of the increasing openness and sharing of data; and 6) the problematics and understandings of user a”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
The length, font size, and number of headings in the TACs would in all probability discourage engagement, so users would accept TACs without reading them.
“This study found that the length, font size, and number of headings used in these TACs would, in all probability, discourage engagement, so that users would accept the TACs without reading them.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
There was no indication whether EdX's use of student posts would include depersonalization of the data.
“There was no indication whether that use would include a depersonalization of the data.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
All three providers acknowledged using cookies to collect data, but only FutureLearn provided a list of cookies with clear descriptions of their function.
“Interestingly, while all three providers acknowledged that they used cookies to collect data, only one (FutureLearn) provided a list of cookies with clear descriptions of their function.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
One MOOC provider explicitly stated they may request information from third parties such as credit reference agencies.
“One MOOC provider explicitly stated that they may request information from third parties such as credit reference agencies.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
EdX alerted users that student posts are owned by EdX in perpetuity and could be used for whatever purposes EdX decides.
“One provider (EdX) alerted users that student posts are owned by EdX in perpetuity and could be used for whatever purposes EdX decides.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
All three providers made clear that disabling cookies would disrupt the service, potentially rendering it unusable.
“All three providers made it clear that disabling the cookies would disrupt the service, potentially to the extent that it would be rendered unusable.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
All three MOOC providers stated that all information provided by users would be collected and used.
“All three providers stated clearly that all information provided by users would be collected and used.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
Only FutureLearn provided an opportunity, for a small fee, for users to request access to data held by the company.
“Only one, FutureLearn, provided an opportunity (for a small fee) for users to request access to the data held by the company (in line with national data protection requirements).”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
Despite concerns about the effectiveness of regulation and legislation, the role of substantive rules and hard boundaries that block particularly troublesome practices, as well as softer default rules that can be bargained around, cannot altogether be negated.
“In following Solove (2013), Prinsloo and Slade suggest that despite concerns about the effectiveness of regulation and legislation, we cannot altogether negate the role of substantive rules and hard boundaries”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
FutureLearn encouraged users to reveal personal details such as gender, location, and history to deepen the personal element in learning.
“Interestingly, and of some concern, was the encouragement by FutureLearn for users to reveal personal details such as gender, location, and history in order to deepen the personal element in the learning.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
None of the providers gave the option to opt out; users were alerted that their MOOC provider could not ensure information shared would not be used by fellow users or made public due to security breaches.
“None of the providers gave the option to opt out -indeed, users were alerted to the inability of their MOOC provider to ensure and guarantee that information shared would not be used by fellow users or made public because of security breaches.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
All three providers stated collected data would be used to increase efficiency of the platform and structure of the learning experience.
“Regarding the collected data, all three stated that it would be used to increase the efficiency of the platform and the structure of the learning experience.”
From Student Vulnerability, Agency and Learning Analytics: An Exploration (2016) · cites this paper as Prinsloo & Slade, 2015
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‹Da habe ich es dann einfach ausgeschaltet›
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- Authors
- Prinsloo, P., & Slade, S.
- Affiliations
- University of South Africa (ZA)
- The Open University (GB)
- Venue
- Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
- Publisher
- ACM
- Published
- 2015-03-16
- Pages
- 83-92
- OpenAlex
- W2142141517
- Cited by
- 119
- References
- 63
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 9 · 2023: 11 · 2022: 9 · 2021: 11 · 2020: 10 · 2019: 13 · 2018: 10 · 2017: 6 · 2016: 20 · 2015: 2
- FWCI
- 46.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- Open Research Online (The Open University) (acceptedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Law › Data Privacy and Cybersecurity
- Keywords
- student privacy, self-management, student data, online tracking, mass surveillance, panopticon, student success, student retention, data harvesting, higher education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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