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

Autor/inn/en	Crick, Ruth Deakin; Knight, Simon; Barr, Steven
Titel	Towards Analytics for Wholistic School Improvement: Hierarchical Process Modelling and Evidence Visualization
Quelle	In: Journal of Learning Analytics, 4 (2017) 2, S.160-188 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Educational Improvement; Holistic Approach; Data Collection; Data Analysis; Foreign Countries; Visualization; Evaluation Methods; Case Studies; Technology Uses in Education; Self Evaluation (Groups); Models; National Standards; Decision Making; Engineering Education; Leadership Role; Systems Approach; Computer Software; Transformative Learning; Measurement Techniques; Questionnaires; Interviews; College Students; College Faculty; United Kingdom (England) + Suchen Sie Ihr Suchwort? Teaching improvement; Unterrichtsentwicklung; Holistischer Ansatz; Data capture; Datensammlung; Auswertung; Ausland; Visualisation; Visualisierung; Case study; Fallstudie; Case Study; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Self evaluation; Group; Groups; Selbstevaluation; Gruppe (Soz); Analogiemodell; Decision-making; Entscheidungsfindung; Ingenieurausbildung; Systemischer Ansatz; Pädagogische Transformation; Messtechnik; Fragebogen; Interviewing; Interviewtechnik; Collegestudent; Fakultät
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. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01