(This post is by Andrew Blair, Head of IT Services and Solutions, Zurich International School)
With over 1200 K-12 students and 280 employees, Zurich International School is an International Baccalaureate (IB) Organization World School and is accredited by the New England Association of Schools and Colleges (NEASC) Commission on International Education. ZIS is also approved by the Department of Education of the Canton of Zurich, Switzerland. It delivers a strong academic program, centered around the traditional core disciplines, preparing students for entrance into colleges, universities, or other institutions.
Due to an increasing collection of academic performance data from various internal and external sources, ZIS is continually seeking ways to improve the collation, correlation and readability of separate data sets for the purpose of informed decision making. Internal data sources include teacher-assessed subject grades, descriptors for standards and achievement levels for instructional areas. External data sources include WRaP ERB, ISA, IB, and NWEA MAP. It is very evident that when data from those different sources are treated in isolation, identifying trends and patterns in relation to both the individual student and grade level cohorts is extremely challenging. Optimising academic performance data into a single online solution is therefore a key goal in the school’s data governance strategy.
To explore options in greater depth, ZIS formed a working committee of faculty members, from various disciplines and roles, to research the evolving theme of Learning Analytics. This led to extensive internal data audits, evaluations of data flows and integrations between systems, and written reviews of data-usage within the school. Committee members attended workshops, conferences and, in some cases, delivered presentations. The committee also sought third-party learning analytics solutions which ultimately resulted in our membership with Consilience and the Learning Analytics Collaborative (LAC).
In using the tools provided by LAC, academic progress data related to individual students is brought together in a single screen. Without having to access multiple sources of data, authorised users are able to quickly view the historical and recent achievement of any particular student. Additional screeners of large datasets also offer opportunities to evaluate performance across cohorts, and to rank and cluster students. The on-screen visuals return the raw data in formats that are easily read by all users regardless of experience with analytics.
Through this process, ZIS is learning about the potential of its own data. The analytics and visuals established with LAC have set a benchmark for optimising our core performance data, and is proving to enhance discussions among faculty and help inform decision-making. The school is now better positioned for establishing and maintaining secure processes that ensure the integrity of data and protect individual data subject rights. The future dictates that data presented in this format will extend beyond faculty, and to students and parents; better informing families of academic progress and supporting student choices for individual learning paths and goal setting.