Downstream Consent: A Better Legal Framework for Big Data

Cormack, Andrew

doi:10.21039/irpandp.v1i1.9

Cited by 16 publications

(13 citation statements)

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“…For informed consent to be valid, it must be freely given and informed (Cormack, ; Drachsler & Greller, ; Faden & Beauchamp, ; Gallagher et al , ; Miller & Wertheimer, ). Consent requires understanding and “may no longer provide adequate protection and guidance either for individuals or for organizations” if such understanding is not achieved (Cormack, ). Faden and Beauchamp () summarize the autonomous authorization (AA) model of consent by writing that, “X acts autonomously only if X acts 1) intentionally, 2) with understanding and 3) without controlling influences.”…”

Section: Resultsmentioning

confidence: 99%

Ethics in educational technology research: Informing participants on data sharing risks

Beardsley¹,

Santos²,

Hernández‐Leo³

et al. 2019

Brit J Educational Tech

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Participants in educational technology research regularly share personal data which carries with it risks. Informing participants of these data sharing risks is often only done so through text contained within a consent form. However, conceptualizations of data sharing risks and knowledge of responsible data management practices among teachers and learners may be impoverished-limiting the effectiveness of a consent form in communicating such risks in a manner that adequately supports participants in making informed decisions about sharing their data. At two high schools participating in an educational research project involving the use of technology in the classroom, we investigate teacher and student conceptions of data sharing risks and knowledge of responsible data management practices; and introduce a communication approach that attempts to better inform educational technology research participants of such risks. Results of this study suggest that most teachers have not received formal training related to responsibly managing data; and both teachers and students see the need for such training as they come to realize that their understanding of responsible data management is underdeveloped. Thus, efforts beyond solely explaining data sharing risks in an informed consent form may be needed in educational technology research to facilitate ethical self-determination.

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Section: Resultsmentioning

confidence: 99%

Ethics in educational technology research: Informing participants on data sharing risks

Beardsley¹,

Santos²,

Hernández‐Leo³

et al. 2019

Brit J Educational Tech

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show abstract

“…“For communities and states, data are a means by which political agendas and work can be legitimised, conducted and contested by enabling the construction of evidence‐informed narratives and counter‐discourses that have greater rhetorical value than anecdote or sentiment” (Kitchin, , p. 16). (Also see Cormack, ; Fairfield, ). As such, databases become agents acting as “immutable mobiles” (Kitchin, , p. 22) and as “relational entities” (Kitchin, , p. 23).…”

Section: Towards a Social Cartographymentioning

confidence: 98%

“…(Also see Cormack, 2016;Fairfield, 2017). As such, databases become agents acting as "immutable mobiles" (Kitchin, 2014, p. 22) and as "relational entities" (Kitchin, 2014, p. 23).…”

Section: Analytics As Performative Politicsmentioning

confidence: 99%

A social cartography of analytics in education as performative politics

Prinsloo¹

2019

Brit J Educational Tech

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Data—their collection, analysis and use—have always been part of education, used to inform policy, strategy, operations, resource allocation, and, in the past, teaching and learning. Recently, with the emergence of learning analytics, the collection, measurement, analysis and use of student data have become an increasingly important research focus and practice. With (higher) education having access to more student data, greater variety and nuanced/granularity of data, as well as collecting and using real‐time data, it is crucial to consider the data imaginary in higher education, and, specifically, analytics as performative politics. Data and data analyses are often presented as representing “reality” and, as such, are seminal in institutional “truth‐making,” whether in the context of operational or student learning data. In the broader context of critical data studies (CDS), this social cartography examines and maps the “data frontier” and the “data gaze” within the context of the dominant narrative of evidence‐based management and the data imaginary in higher education. Following an analysis of the main assumptions in evidence‐based management and the power of metrics, this paper presents a social cartography of data analytics not only as representational, but as actant, and as performative politics. What is already known about this topic Student data in higher education are increasingly used not only to inform operational and strategic planning, but to inform and shape curricula, pedagogy and student learning. The collection, measurement, analysis and use of student data are informed by a particular data imaginary in service of evidence‐based management. Data and analytics are seen as objective, neutral representations of reality and used as basis for informing policy, operational planning, resource allocation, and, increasingly, learning analytics and student facing dashboards. What this paper adds Critically examines data analytics in higher education as “truth‐making” and never neutral, and always as provisional snapshots of “reality” serving particular, often undeclared, assumptions and interests. Situates learning analytics as the collection, measurement, analysis and use of student data in the broader context of the data imaginary and student data as “data frontier”. Provides evidence that data analytics is not only representational, but, increasingly, performative and political. Implications for practice and/or policy Makes explicit some of the theoretical, political, epistemological and ontological assumptions underpinning and sustaining the data imaginary in higher education. Foregrounds the need for more critical approaches to our expectations, collections, analyses and use of student data. Argues the need for policy and practitioners to recognise the inherent political and performative nature of data and data analysis, and the need for transparency, accountability and care.

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“…This has also led to a question of timing as to when consent should be sought [32]. In light of this issue and in response to the consent requirements in the General Data Protection Regulation 2016/679, the UK non-profit educational consultancy Jisc suggests that institutions should seek 'downstream consent' (consent for personalised intervention), as there is usually clearer information about consequences on individuals at this stage than in the phase of finding patterns in data [7].…”

Section: Issues In the Adoption Of Learning Analyticsmentioning

confidence: 99%

Policy Matters: Expert Recommendations for Learning Analytics Policy

Scheffel

Tsai

Gašević

et al. 2019

Lecture Notes in Computer Science

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Interest in learning analytics (LA) has grown rapidly among higher education institutions (HEIs). However, the maturity levels of HEIs in terms of being 'student data-informed' are only at early stages. There often are barriers that prevent data from being used systematically and effectively. To assist higher education institutions to become more mature users and custodians of digital data collected from students during their online learning activities, the SHEILA framework, a policy development framework that supports systematic, sustainable and responsible adoption of LA at an institutional level, was recently built. This paper presents a mix-method study using a group concept mapping (GCM) approach that was conducted with LA experts to explore essential features of LA policy in HEI in contribution the development of the framework. The study identified six clusters of features that an LA policy should include, provided ratings based on ease of implementation and importance for each of the six themes, and offered suggestions to HEIs how they can proceed with the development of LA policies.

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Downstream Consent: A Better Legal Framework for Big Data

Cited by 16 publications

References 0 publications

Ethics in educational technology research: Informing participants on data sharing risks

Ethics in educational technology research: Informing participants on data sharing risks

A social cartography of analytics in education as performative politics

Policy Matters: Expert Recommendations for Learning Analytics Policy

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