Identification of ‘at risk’ students using learning analytics: the ethical dilemmas of intervention strategies in a higher education institution

Lawson, Celeste; Beer, Colin; Rossi, Dolene; Moore, Teresa; Fleming, Julie

doi:10.1007/s11423-016-9459-0

Cited by 73 publications

(73 citation statements)

References 8 publications

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“…There are a range of uses for LA, including, most commonly, identifying at-risk students to assist in retention (examples include articles by Arnold & Pistilli, 2012;Lawson, Beer, Rossi, Moore, & Fleming, 2016;Prinsloo, Archer, Barnes, Chetty, & van Zyl, 2015) and related to this, educational triage, where institutions utilise data to make decisions about where to best use limited resources (Prinsloo & Slade, 2014). However, it can be used to serve learning (Booth, 2012;Slade & Prinsloo, 2013); to personalise learning (Pardo & Siemens, 2014); provide improved choices for students to respond faster to actionable insights (Oblinger, 2012;Prinsloo et al, 2015) and improve learning and teaching practice and curriculum (Bronnimann, West, Huijser, & Heath, 2018;Prinsloo et al, 2015).…”

Section: Ethical Implicationsmentioning

confidence: 99%

Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics

West

Luzeckyj

Toohey

et al. 2020

AJET

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Increasingly learning analytics (LA) has begun utilising staff- and student-facing dashboards capturing visualisations to present data to support student success and improve learning and teaching. The use of LA is complex, multifaceted and raises many issues for consideration, including ethical and legal challenges, competing stakeholder views and implementation decisions. It is widely acknowledged that LA development requires input from various stakeholders. This conceptual article explores the LA literature to determine how student perspectives are positioned as dashboards and visualisations are developed. While the sector acknowledges the central role of students, as demonstrated here, much of the literature reflects an academic, teacher-centric or institutional view. This view reflects some of the key ethical concerns related to informed consent and the role of power translating to a somewhat paternalistic approach to students. We suggest that as students are the primary stakeholders – they should be consulted in the development and application of LA. An ethical approach to LA requires that we engage with our students in their learning and the systems and information that support that process rather than assuming we know we know what students want, what their concerns are or how they would like data presented.

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Section: Ethical Implicationsmentioning

confidence: 99%

Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics

West

Luzeckyj

Toohey

et al. 2020

AJET

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show abstract

“…Data infrastructures and related practices in support of LA surface significant concerns regarding student surveillance and informational privacy (Heath, ). Like privacy issues associated with data mining in other contexts, LA also raise questions regarding individual autonomy and informational controls (Pardo & Siemens, ; Rubel & Jones, ) in ways that bring to the fore power, fairness, and transparency concerns (Lawson, Beer, Rossi, Moore, & Fleming, ). The latter points are especially apropos given the black‐boxed (and potentially biased) nature of LA technologies and the increasing reliance by HEIs on vendors who help manage and profit from a glut of student data (Mittelstadt, ; Pasquale, ; see Johnson, ).…”

Section: Student Perceptions and Moral Questionsmentioning

confidence: 99%

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Jones

Rubel

LeClere

2019

Asso for Info Science & Tech

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Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.

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“…Also significant was the decision of the journal, Educational Technology, Research and Development to dedicate a whole issue in 2016 (Volume 65, issue 4) to the ethical considerations and implications in/of LA. Central to all of the articles in this volume is the issue of consent, informed consent, and various propositions for students' access and control of their data (Ifenthaler & Tracey, 2016;Lawson, Beer, Rossi, Moore, & Fleming, 2016;Scholes, 2016;. In their editorial to a special issue dedicated to "Ethics and Privacy in Learning Analytics" in the Journal of Learning Analytics, (Ferguson et al, 2016) mention as a challenge the need to gain informed consent and to "limit time for which data are held before destruction and for which consent is valid" (p. 9).…”

Section: Learning Analyticsmentioning

confidence: 99%

Student Consent in Learning Analytics

Prinsloo¹,

Slade²

2018

Learning Analytics in Higher Education

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Identification of ‘at risk’ students using learning analytics: the ethical dilemmas of intervention strategies in a higher education institution

Cited by 73 publications

References 8 publications

Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics

Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Student Consent in Learning Analytics

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