Sins of omission: Critical informatics perspectives on privacy in e‐learning systems in higher education

Paris, Britt; Reynolds, Rebecca; McGowan, Catherine

doi:10.1002/asi.24575

Cited by 24 publications

(21 citation statements)

References 56 publications

(104 reference statements)

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“…As we think about the learning outcome of “agency” in particular, we draw from Paris, Reynolds and McGowan’s (2021) critical informatics interpretation of Feenberg’s notion of technical citizenship (2017a) that proposes a mode of “conscious co-production” (p. 11) where the actions of users have an ability to affect the codes and designs that define roles of the users within the technological network. The authors (2021) note that per Feenberg, this conscious co-production of systems from below results when ordinary, non-experts are enrolled in technological networks in ways that encourage them to develop a situated, practical knowledge of the network itself and avenues to exercise this knowledge (p. 3).…”

Section: Literature Review: a Need For Ecological Literacymentioning

confidence: 99%

Disinformation detox: teaching and learning about mis- and disinformation using socio-technical systems research perspectives

Paris

Reynolds

Marcello

2022

ILS

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Purpose This paper aims to address some limitations in existing approaches to the study of mis- and dis-information and offers what the authors propose as a more comprehensive approach to framing and studying these issues, geared toward the undergraduate level of learner. In doing so, the authors prioritize social shaping of technology and critical informatics perspectives as lenses for explicating and understanding complex mis- and dis-information phenomena. One purpose is to offer readers an understanding of the mis- and dis-information studies landscape, and advocate for the merit of taking the given approach the authors outline. Design/methodology/approach The paper builds upon design-based research (DBR) methods. In this paper, the authors present the actual curriculum that will be empirically researched in 2022 and beyond in a program of iterative DBR. Findings Findings of this conceptual paper comprise a fully articulated undergraduate syllabus for a course the authors entitled, “Disinformation Detox.” The authors will iterate upon this curriculum development in ongoing situated studies conducted in undergraduate classrooms. Originality/value The value and originality of this article is in its contribution of the ontological “innovation” of a way of framing the mis- and dis-information knowledge domain in terms of social shaping and critical informatics theories. The authors argue that the proposed approach offers students the opportunity to cultivate a complex form of what Milner and Phillips describe as “ecological literacy” that is in keeping with the mis- and dis-information problem domain.

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Section: Literature Review: a Need For Ecological Literacymentioning

confidence: 99%

Disinformation detox: teaching and learning about mis- and disinformation using socio-technical systems research perspectives

Paris

Reynolds

Marcello

2022

ILS

Self Cite

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“…Paris et al [ 13 ] focused their research on the privacy problems that arose because of the pandemic's spread in higher education. They observed higher institutions' deployment of popular online learning platforms using critical informatics methodologies and theories to find numerous trends that emerged as a consequence of their implementation.…”

Section: Related Literaturementioning

confidence: 99%

Cyber Risk Recommendation System for Digital Education Management Platforms

Yin¹,

Chen²

2022

Computational Intelligence and Neuroscience

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Covid-19 pandemic has ushered in a new school and academic year for students in a distance learning regime. This new daily routine was unprecedented and undoubtedly unusual, especially for the younger ones. At this point and at these ages, the risk of cyber fraud is even greater. The transition from the physical environment to the Internet took place quickly without the appropriate time to control potential risks and the proper information and training of teachers and students. Some common threats that need to be addressed to protect learners and their data when using e-learning methods are malicious remote access, malware, phishing, cyber fraud, etc. Considering the above situation, this work presents an innovative cyber risk recommendation system for digital education management platforms. The system in question is a distributed two-stage algorithm based on game theory and machine learning, which is trained by the constant change in the choice of recommendations by users to maximize security. We examine the algorithm’s ability to simulate a user system in which everyone independently selects a user recommendation, assesses the environment and the implications of this choice, and then concludes whether it will continue to have that recommendation fixed. The methodology with which we have represented the digital e-learning system has been done with an approach that directly corresponds with their general view as a cyber-physical-social system. We consider the digital school as an environment that brings limitations, leading us to a pretty demanding personalization problem. Users coexist in this environment, in which everyone acts voluntarily but influences and is influenced by the surrounding environment. Our results lead us to conclude that this algorithm responds in a fully effective, flexible, and efficient way to the needs of protection and risk assessment of e-learning education systems.

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“…Many rely on commercial platform providers to provide the digital infrastructure, support and analytical services offering “speedy, accessible, revealing, panoramic, prophetic and smart” solutions (Beer, 2019, p. 22). Whilst the protection of student data and privacy presumes the functioning of the LMS behind an institutional firewall, that firewall may already have allowed access for the Trojan horse through the use of integrated platforms such as Microsoft (Paris et al., 2021) and Google Classroom (Eastman & Hansen, 2021).…”

Section: Situating Privacy In the Context Of Learning Analyticsmentioning

confidence: 99%

The answer is (not only) technological: Considering student data privacy in learning analytics

Prinsloo

Slade

Khalil

2022

Brit J Educational Tech

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Evidence shows that appropriate use of technology in education has the potential to increase the effectiveness of, eg, teaching, learning and student support. There is also evidence that technology can introduce new problems and ethical issues, eg, student privacy. This article maps some limitations of technological approaches that ensure student data privacy in learning analytics from a critical data studies (CDS) perspective. In this conceptual article, we map the claims, grounds and warrants of technological solutions to maintaining student data privacy in learning analytics. Our findings suggest that many technological solutions are based on assumptions, such as that individuals have control over their data (‘data as commodity’), which can be exchanged under agreed conditions, or that individuals embrace their personal data privacy as a human right to be respected and protected. Regulating student data privacy in the context of learning analytics through technology mostly depends on institutional data governance, consent, data security and accountability. We consider alternative approaches to viewing (student) data privacy, such as contextual integrity; data privacy as ontological; group privacy; and indigenous understandings of privacy. Such perspectives destabilise many assumptions informing technological solutions, including privacy enhancing technology (PET). What is already known about this topic Various actors (including those in higher education) have access to and collect, use and analyse greater volumes of personal (student) data, with finer granularity, increasingly from multiplatforms and data sources. There is growing awareness and concern about individual (student) privacy. Privacy enhancing technologies (PETs) offer a range of solutions to individuals to protect their data privacy. What this paper adds A review of the assumption that technology provides adequate or complete solutions for ensuring individual data privacy. A mapping of five alternative understandings of personal data privacy and its implications for technological solutions. Consideration of implications for the protection of student privacy in learning analytics. Implications for practice and/or policy Student data privacy is not only a technological problem to be solved but should also be understood as a social problem. The use of PETs offers some solutions for data privacy in learning analytics. Strategies to protect student data privacy should include student agency, literacy and a whole‐system approach.

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Sins of omission: Critical informatics perspectives on privacy in e‐learning systems in higher education

Abstract: after first online publication: The text "Anonymized" has been changed to Rutgers University and the tables are moved to relevant section.]

Cited by 24 publications

References 56 publications

Disinformation detox: teaching and learning about mis- and disinformation using socio-technical systems research perspectives

Disinformation detox: teaching and learning about mis- and disinformation using socio-technical systems research perspectives

Cyber Risk Recommendation System for Digital Education Management Platforms

The answer is (not only) technological: Considering student data privacy in learning analytics

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