Towards Analytics for Wholistic School Improvement: hierarchical Process Modelling and Evidence Visualisation

Ruth Crick, Simon Knight, Steven Barr

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Central to the mission of most educational institutions is 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 qualities. 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 sensemaking and decisioning. Taking a systems perspective, the Hierarchical Process Modelling (HPM) approach and the 'Perimeta' visual analytic provides a dashboard that informs leadership decisioning with heterogeneous, often incomplete evidence. We report a prototype of the Perimeta modeling 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, and areas of uncertainty. We discuss the nature of the modelling decisions and implicit values involved in quantifying these kinds of educational outcomes.
Original languageEnglish
Pages (from-to)160-188
Number of pages28
JournalJournal of Learning Analytics and Knowledge
Volume4
Issue number2
Publication statusPublished - 1 Jul 2017

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