Towards Analytics for Wholistic School Improvement: Hierarchical Process Modelling and Evidence Visualization
Towards Analytics for Wholistic School Improvement: Hierarchical Process Modelling and Evidence Visualization
复制标题
实现学校整体改进的分析：分层过程建模和证据可视化
DOI：
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发表时间：
2017
影响因子：
3.9
通讯作者：
Steven Barr
中科院分区：
文献类型：
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作者：
R. Crick;Simon Knight;Steven Barr
大多数教育机构的核心任务是培养下一代公民为社会做贡献。学校、学院和大学重视一系列的成果——例如，解决问题、创造力、协作、公民意识、社区服务——以及传统学科的学术成果。这些通常被称为“更广泛的结果”，很难量化。虽然新型监测技术和公共数据集扩大了量化这些指数的可能性，但我们需要将这些数据汇集在一起，以支持意义制定和决策。从系统的角度来看，分层过程建模（HPM）方法和“Perimeta”可视化分析提供了一个仪表板，可以用异构的、通常不完整的证据通知领导层决策。我们报告了一个来自教育的Perimeta模型的原型，在学校网络中汇总了更广泛的结果数据，并计算了它们对关键绩效指标的累积贡献，使用意大利国旗的视觉分析不仅明确了支持证据，而且明确了具有挑战性的证据，以及不确定的领域。我们讨论了建模决策的性质以及量化这些教育成果所涉及的隐含价值。
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.