Retracing the Evolution of Thinking Ethically About Student Data

Prinsloo, Paul; Slade, Sharon

doi:10.1002/ir.20305

Cited by 4 publications

(4 citation statements)

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“…While literature points to the impact of the lack of theory in learning analytics (Gašević et al , 2019); what has not yet been explored yet is how institutions’ understanding of student success and the interrelationships of factors that may impact student success and shape how trustworthy learning analytics is (Archer and Prinsloo, 2020). There is a danger that we assume the data will speak for itself and that institutions do not need to have a research-informed understanding of student success (Prinsloo and Slade, 2019). Currently, the dominant theory in learning analytics research is self-regulated learning (Khalil et al , 2022) and while it falls outside the scope of this article to explore how learning analytics would play out should student learning be understood differently, Broughan and Prinsloo, 2020 suggest that the type of data collected may provide a glimpse of how institutions understand student learning.…”

Section: Discussionmentioning

confidence: 99%

“Trust us,” they said. Mapping the contours of trustworthiness in learning analytics

Slade,

Prinsloo,

Khalil

2023

ILS

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Purpose The purpose of this paper is to explore and establish the contours of trust in learning analytics and to establish steps that institutions might take to address the “trust deficit” in learning analytics. Design/methodology/approach “Trust” has always been part and parcel of learning analytics research and practice, but concerns around privacy, bias, the increasing reach of learning analytics, the “black box” of artificial intelligence and the commercialization of teaching and learning suggest that we should not take stakeholder trust for granted. While there have been attempts to explore and map students’ and staff perceptions of trust, there is no agreement on the contours of trust. Thirty-one experts in learning analytics research participated in a qualitative Delphi study. Findings This study achieved agreement on a working definition of trust in learning analytics, and on factors that impact on trusting data, trusting institutional understandings of student success and the design and implementation of learning analytics. In addition, it identifies those factors that might increase levels of trust in learning analytics for students, faculty and broader. Research limitations/implications The study is based on expert opinions as such there is a limitation of how much it is of a true consensus. Originality/value Trust cannot be assumed is taken for granted. This study is original because it establishes a number of concerns around the trustworthiness of learning analytics in respect of how data and student learning journeys are understood, and how institutions can address the “trust deficit” in learning analytics.

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Section: Discussionmentioning

confidence: 99%

“Trust us,” they said. Mapping the contours of trustworthiness in learning analytics

Slade,

Prinsloo,

Khalil

2023

ILS

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“…The board would consist of individuals with technical and context expertise and key stakeholders to manage oversight (Mathies, 2018). Prinsloo and Slade(2019) cautioned that a formal data policy addressing the risks of using PAs is essential when creating an ethical data strategy and recommended the creation of a data oversight committee to ensure adherence to the policy throughout the institution.…”

Section: Ethical Data Strategymentioning

confidence: 99%

“…PAs combine LMS and institutional data to determine a student’s ability to succeed within a course or program, and to support institutional decision-making (Fritz and Whitmer, 2019; West et al , 2020). Without human oversight, PA predictions may sometimes turn students into digital commodities by removing the human aspect from data analysis (Prinsloo and Slade, 2019). In addition, if student data are unstable or inaccurate, a vetting process is needed to test the accuracy of a PA’s recommendation (Mathies, 2018; Prinsloo and Slade, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Without human oversight, PA predictions may sometimes turn students into digital commodities by removing the human aspect from data analysis (Prinsloo and Slade, 2019). In addition, if student data are unstable or inaccurate, a vetting process is needed to test the accuracy of a PA’s recommendation (Mathies, 2018; Prinsloo and Slade, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Predictive algorithms and racial bias: a qualitative descriptive study on the perceptions of algorithm accuracy in higher education

von Winckelmann

2023

ILS

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Purpose This study aims to explore the perception of algorithm accuracy among data professionals in higher education. Design/methodology/approach Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended questionnaires and six semi-structured interviews. Participants included higher education professionals who have worked with predictive algorithm (PA) recommendations programmed with student data. Findings Participants are aware of systemic and racial bias in their PA inputs and outputs and acknowledge their responsibility to ethically use PA recommendations with students in historically underrepresented groups (HUGs). For some participants, examining these topics through the lens of social justice was a new experience, which caused them to look at PAs in new ways. Research limitations/implications Small sample size is a limitation of the study. Implications for practice include increased stakeholder training, creating an ethical data strategy that protects students, incorporating adverse childhood experiences data with algorithm recommendations, and applying a modified critical race theory framework to algorithm outputs. Originality/value The study explored the perception of algorithm accuracy among data professionals in higher education. Examining this topic through a social justice lens contributes to limited research in the field. It also presents implications for addressing racial bias when using PAs with students in HUGs.

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2020

Cases in Pediatric Acute Care

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Retracing the Evolution of Thinking Ethically About Student Data

Cited by 4 publications

References 31 publications

“Trust us,” they said. Mapping the contours of trustworthiness in learning analytics

“Trust us,” they said. Mapping the contours of trustworthiness in learning analytics

Predictive algorithms and racial bias: a qualitative descriptive study on the perceptions of algorithm accuracy in higher education

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