Paul Prinsloo, Unisa and Sharon Slade, Open University
consider the issues associated with distance learning, retention and time taken to complete. Students as the walking wounded and HE as the battlefield. Are students walking around with invisible triage tags attached that only lecturers can see? Is this fair? Or just pragmatic? Attention (resource) is finite. How do we make moral decisions about how we allocate resource? The answer lies in the data that we can collect. To identify students who are at risk and to do something before they drop out.
Against a changing context, HEIs need to make decisions about how to allocate resource, we must do more with less against increasing inspection, funding follows rather than precedes performance. To the rescue, learning analytics.
lots of hype, student data as the ‘new black’. Lots of issues, privacy, govern mentality, data protection and other ethical concerns. Plus, is much of this unproven? Introduces the concept of triage – balancing the futility or impact of the intervention with the number of patients requiring care, the scope of care required and the resources available to provide care.
Four basic principles, respect patient(/student) autonomy, beneficence, non malificence, justice (care not determined by privilege, status, gender etc). Is that enough? Perhaps not, we must make choices about how we allocate resources. Consider also issues of transparency (knowledge of criteria), stakeholder acceptance of rationale, mechanism for appeals and challenges, oversight(preferably external). Can we apply historical principle of triage to education?
in trying to achieve this, do we always assume that HEIs are fair? Student retention/failure are the result of complex issues, so how do we make choices? Do we see the student as at fault rather than the institution? If success is determined by algorithms alone, is this at odds with the underlying ethos of openness?
Four principles to guide decisions: student and instructional autonomy are situated and bounded (within their context); beneficence ( in the best interest of the student) – data doesn’t give the whole picture; non malificence and transparency – can mean, is it fair to allow a student to fail if we ‘know’ they are doomed?; distributive justice (race, gender and class do not matter)
its a a moral tightrope – the reality of resource constraints + we cannot afford NOT to use data + our data and algorithms cannot be complete, do not provide the full picture + student success is not a linear process.
Need to move beyond notions of justice to an ethics of care.