Consequentialism

Context (archaeology)

Flourishing

Engineering

Analytics

Pure mathematics

Biology

Social psychology

Public relations

Face (sociological concept)

Field (mathematics)

Pedagogy

Telecommunications

Computer science

Deontological ethics

Paleontology

Educational technology

Applied ethics

Mechanical engineering

Psychology

Mathematics

Political science

Work (physics)

Learning analytics

Sociology

Data science

Knowledge management

Information ethics

Engineering ethics

Frame (networking)

Social science

British Journal of Educational Technology

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. Practitioner Notes 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.

Practical ethics for building learning analytics