Towards Analytics for Wholistic School Improvement: Hierarchical Process Modelling and Evidence Visualization

Ruth Elizabeth Deakin Crick
Simon Knight
Steven Barr

Abstract


Central to the mission of most educational institutions is the task of preparing the next generation of citizens to contribute to society. Schools, colleges, and universities value a range of outcomes — e.g., problem solving, creativity, collaboration, citizenship, service to community — as well as academic outcomes in traditional subjects. Often referred to as “wider outcomes,” these are hard to quantify. While new kinds of monitoring technologies and public datasets expand the possibilities for quantifying these indices, we need ways to bring that data together to support sense-making and decision-making. Taking a systems perspective, the hierarchical process modelling (HPM) approach and the “Perimeta” visual analytic provides a dashboard that informs leadership decision-making with heterogeneous, often incomplete evidence. We report a prototype of Perimeta modelling from education, aggregating wider outcomes data across a network of schools, and calculating their cumulative contribution to key performance indicators, using the visual analytic of the Italian flag to make explicit not only the supporting evidence, but also the challenging evidence, as well as areas of uncertainty. We discuss the nature of the modelling decisions and implicit values involved in quantifying these kinds of educational outcomes.

Keywords


Learning analytics; academic analytics; leadership; decision support; complex systems; educational values; visualization; dashboard; uncertainty; risk; surveys

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References


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DOI: http://dx.doi.org/10.18608/jla.2017.42.13

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