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ERIC Number: EJ1149400
Record Type: Journal
Publication Date: 2017
Pages: 29
Abstractor: As Provided
ISSN: EISSN-1929-7750
Towards Analytics for Wholistic School Improvement: Hierarchical Process Modelling and Evidence Visualization
Crick, Ruth Deakin; Knight, Simon; Barr, Steven
Journal of Learning Analytics, v4 n2 p160-188 2017
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: United Kingdom (England)
Grant or Contract Numbers: N/A