Ethical Challenges for Learning Analytics

Ferguson, Rebecca

doi:10.18608/jla.2019.63.5

Cited by 55 publications

(48 citation statements)

References 11 publications

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“…These concepts highlight potential points of adverse impact, from why and how the data is collected, where it is stored, who has access, what is done to it, and what action results. There are still many unresolved issues relating to the ethics of learning analytics, such as the duty an institution has to act on and/or use data that is collected, and an ongoing debate about the validity of the algorithms that are used to generate analyses for learning analytics (Ferguson, 2019). Further examination to identify unforeseen or unintended consequences from learning analytics systems and techniques are important in minimising adverse outcomes for students and staff.…”

Section: Addressing the Potential Of Negative Impactmentioning

confidence: 99%

Learning Analytics: Pathways to Impact

Corrin

Scheffel

Gašević

2020

AJET

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The field of learning analytics has evolved over the past decade to provide new ways to view, understand and enhance learning activities and environments in higher education. It brings together research and practice traditions from multiple disciplines to provide an evidence base to inform student support and effective design for learning. This has resulted in a plethora of ideas and research exploring how data can be analysed and utilised to not only inform educators, but also to drive online learning systems that offer personalised learning experiences and/or feedback for students. However, a core challenge that the learning analytics community continues to face is how the impact of these innovations can be demonstrated. Where impact is positive, there is a case for continuing or increasing the use of learning analytics, however, there is also the potential for negative impact which is something that needs to be identified quickly and managed. As more institutions implement strategies to take advantage of learning analytics as part of core business, it is important that impact can be evaluated and addressed to ensure effectiveness and sustainability. In this editorial of the AJET special issue dedicated to the impact of learning analytics in higher education, we consider what impact can mean in the context of learning analytics and what the field needs to do to ensure that there are clear pathways to impact that result in the development of systems, analyses, and interventions that improve the educational environment.

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Section: Addressing the Potential Of Negative Impactmentioning

confidence: 99%

Learning Analytics: Pathways to Impact

Corrin

Scheffel

Gašević

2020

AJET

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“…The LAK '18 conference made a move to open up this area for discussion by inviting Neil Selwyn, expert in the sociology of technology use in educational settings, as a keynote speaker. His talk on "The Promises and Problems of Learning Analytics" opened up a discussion within the community, which has been continued and amplified by this journal in an Invited Dialogue Special Section in the current issue (Essa, 2019;Ferguson, 2019;Prinsloo, 2019;Rosé, 2019;Selwyn, 2019).…”

Section: Discussionmentioning

confidence: 98%

“…The finding that there was substantial discomfort and unease around learning analytics led the authors to raise the issue with the Society for Learning Analytics Research and the LAK program chairs. One response to this was the invitation to Neil Selwyn as a keynote at LAK '18, where he explored some of these feelings of discomfort and how they arise (see also Essa, 2019;Ferguson, 2019;Prinsloo, 2019;Rosé, 2019;and Selwyn, 2019, in an Invited Dialogue Special Section in the current issue).…”

Section: Affectmentioning

confidence: 99%

Moving Forward with Learning Analytics: Expert Views

et al. 2019

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Learning analytics involve the measurement, collection, analysis, and reporting of data about learners and their contexts, in order to understand and optimize learning and the environments in which it occurs. Since emerging as a distinct field in 2011, learning analytics has grown rapidly, and early adopters around the world are developing and deploying these new tools. This paper reports on a study that investigated how the field is likely to develop by 2025 in order to recommend actions to those concerned with the implementation of learning analytics. The study used a Policy Delphi approach, presenting a range of future scenarios to international experts in the field and asking for responses related to the desirability and feasibility of these scenarios, as well as actions that would be required. Responses were received from 103 people from 21 countries. Responses were coded thematically, inter-rater reliability was checked using Cohen's kappa coefficient, and data were recoded if kappa was below 0.6. The seven major themes identified within the data were power, pedagogy, validity, regulation, complexity, ethics, and affect. The paper considers in detail each of these themes and its implications for the implementation of learning analytics. Notes for Practice• This paper analyzes responses from around the world and identifies seven factors that must be taken into account when implementing learning analytics: power, pedagogy, validity, regulation, complexity, ethics, and affect.• Responses revealed widespread unease about how analytics may develop. Bringing people together to engage with and understand the issues will be one way of addressing this problem.• It is also important for different communities to discuss and understand the value of their personal data and to know how they can be used, developed, and protected.• Evidence that learning analytics delivers benefits is not yet convincing to experts in the field.• Major investment of thoughtful effort is required in terms of research agendas and funding, policy and regulation, and developing and informing practice among all those who engage with learning analytics.

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“…In teaching, the data about learning and learners collected on unprecedented scales gave rise to educational data mining and particularly to learning analytics (LA) (Siemens and Long 2011 ). While some argued about the value of learning analytics in informing teachers’ decision-making about pedagogical practices as well as learners’ self-regulation (Ferguson 2012 ; Roll and Winne 2015 ), research also uncovered naïve or even poor pedagogical assumptions on the power of algorithms to predict, support and address learning, which were connected to techno-determinist approaches to data (Ferguson 2019 ; Perrotta and Williamson 2018 ; Selwyn 2019 ). The studies in the field have pointed out how few connections there are between LA models and pedagogical theories (Knight et al 2014 ; Nunn et al 2016 ), the lack of evaluation in authentic contexts, the scant uptake by teachers and learners (Vuorikari et al 2016a , b ) and the social and ethical issues connected to the topic (Broughan and Prinsloo 2020 ; Slade and Prinsloo 2013 ; Prinsloo and Slade 2017 ).…”

Section: Introductionmentioning

confidence: 99%