Practical ethics for building learning analytics

Abstract

Artificial intelligence and data analysis (AIDA) are increasingly entering the field of education. Within this context, the subfield of learning analytics (LA) has, since its inception, had a strong emphasis upon ethics, with numerous checklists and frameworks proposed to ensure that student privacy is respected and potential harms avoided. Here, we draw attention to some of the assumptions that underlie previous work in ethics for LA, which we frame as three tensions. These assumptions have the potential of leading to both the overcautious underuse of AIDA as administrators seek to avoid risk, or the unbridled misuse of AIDA as practitioners fail to adhere to frameworks that provide them with little guidance upon the problems that they face in building LA for institutional adoption. We use three edge cases to draw attention to these tensions, highlighting places where existing ethical frameworks fail to inform those building LA solutions. We propose a pilot open database that lists edge cases faced by LA system builders as a method for guiding ethicists working in the field towards places where support is needed to inform their practice. This would provide a middle space where technical builders of systems could more deeply interface with those concerned with policy, law and ethics and so work towards building LA that encourages human flourishing across a lifetime of learning. What is already known about this topic Applied ethics has a number of well‐established theoretical groundings that we can use to frame the actions of ethical agents, including, deontology, consequentialism and virtue ethics. Learning analytics has developed a number of checklists, frameworks and evaluation methodologies for supporting trusted and ethical development, but these are often not adhered to by practitioners. Laws like the General Data Protection Regulation (GDPR) apply to fields like education, but the complexity of this field can make them difficult to apply. What this paper adds Evidence of tensions and gaps in existing ethical frameworks and checklists to support the ethical development and implementation of learning analytics. A set of three edge cases that demonstrate places where existing work on the ethics of AI in education has failed to provide guidance. A “practical ethics” conceptualisation that draws on virtue ethics to support practitioners in building learning analytics systems. Implications for practice and/or policy Those using AIDA in education should collect and share example edge cases to support development of practical ethics in the field. A multiplicity of ethical approaches are likely to be useful in understanding how to develop and implement learning analytics ethically in practical contexts.

Supporting and challenging learners through pedagogical agents: Addressing ethical issues through designing for values

“…Dealing with these conflicts of interest and protection of the rights of vulnerable people such as children and the mentally disabled, will need carefully designed standards and legislation. Further discussion of data privacy will be left to other articles in this special issue whose focus is on learning analytics (Johanes & Thille,2019;Kitto & Knight, 2019;Prinsloo, 2019;Tsai, Poquet, Gaševid, Dawson, & Pardo, 2019;Williamson, 2019). Hudlicka (2016) identifies several ethical issues that go beyond the general concerns of data privacy and which are specific to virtual agents.…”

Learning analytics and AI: Politics, pedagogy and practices

“…Finally, within the theme of politics, our goal was to garner accounts that recognise the political significance and the potential for power and influence that the intelligence infrastructure constructed through the use of big data, AI and analytics brings to the world. Accounts that would help us to be appropriately vigilant and judiciously embracing with respect to these technologies and the quantifiable approaches they can bring to education (Kitto & Knight, ; Prinsloo, ; Richards & Dignum, ; Tsai et al , ; Williamson, ). We were also keen for clarifications of the way that the three elements of our theme: Politics, Pedagogy and Practices are deeply connected and inter‐related (cf.…”

“…We were also keen for clarifications of the way that the three elements of our theme: Politics, Pedagogy and Practices are deeply connected and inter-related (cf. Johanes & Thille, 2019;Kitto & Knight, 2019).…”

Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education

“…Researchers of LA, AI and education data science have established their potential to reshape and improve teaching, learning and curriculum (Cope & Kalantzis, ; Daniel, ; Lang, Siemens, Wise, & Gasevic, ), recognizing the ethical challenges that need confronting (Kitto & Knight, ; Slade & Prinsloo, ), values‐based approaches to analytics (Richards & Dignum, ) and the importance of theory‐building (Buckingham Shum & Knight, ). Some assessments of digital data in HE have led to ideals of the “smarter university” (Lane, ), while others more critically analyse “neoliberalizing” processes of “marketization” (Komljenovic, ), “unbundling” (McCowan, ) and commercialization made possible by “datafication” (Czerniewicz, ; Selwyn, ), and their role in creating a “student‐consumer” as an “active service‐user” of HE (Tomlinson, , p. 457).…”