Educational Triage in Open Distance Learning: Walking a Moral Tightrope - Prinsloo, Paul; Slade, Sharon (International Review of Research in Open and Distributed Learning, 2014)
Higher education, and more specifically, distance education, is in the midst of a rapidly changing environment. Higher education institutions increasingly rely on the harvesting and analyses of student data to inform key strategic decisions across a wide range of issues, including marketing, enrolment, curriculum development, the appointment of staff, and student assessment. In the light of persistent concerns regarding student success and retention in distance education contexts, the harvesting and analysis of student data in particular in the emerging field of learning analytics holds much promise. As such the notion of educational triage needs to be interrogated. Educational triage is defined as balancing between the futility or impact of the intervention juxtaposed with the number of students requiring care, the scope of care required, and the resources available for care/interventions. The central question posed by this article is “how do we make moral decisions when resources are (increasingly) limited?” An attempt is made to address this by discussing the use of data to support decisions regarding student support and examining the concept of educational triage. Despite the increase in examples of institutions implementing a triage based approach to student support, there is a serious lack of supporting conceptual and theoretical development, and, more importantly, to consideration of the moral cost of triage in educational settings. This article provides a conceptual framework to realise the potential of educational triage to responsibly and ethically respond to legitimate concerns about the “revolving door” in distance and online learning and the sustainability of higher education, without compromising ‘openness.’ The conceptual framework does not attempt to provide a detailed map, but rather a compass consisting of principles to consider in using learning analytics to classify students according to their perceived risk of failing and the potential of additional support to alleviate this risk.