Skip to main content

Data Journey: Discovering Learning Analytics at Zurich International School

(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.         

Comments

Post a Comment

Popular posts from this blog

Using Social and Emotional Data to Promote a Positive School Culture

I came across this great article on Edutopia by Victoria Curry and Mike Setaro on how school leaders can combine traditional data with social and emotional data to get a full picture of the school experience of students and staff. It’s centered around Warm data that gives both dimension and measure to an individual’s and group’s social and emotional status. Opposite to Cool data points, that are a series of structural data sets such as enrollment, attendance, and academic proficiency that typically are the bedrock of school-based analytics. They talk about various examples of Warm data points among them on a matrix with different degrees of pleasantness and energy before engaging in learning (inspired by Mark Brackett’s work). These points can and should be captured, measured and visualized. The insights from this data should be of utmost importance for leaders to find strategies that capture and leverage information related to SEL and interpersonal skills. Harnessing this level of und...
Post a Comment
Read more

Atlas Protocol - Looking at data

The Atlas Protocol is one of the most commonly used structured dialogue formats among schools to facilitate a conversation about data for teachers and other members of the faculty. Learning from Data is a tool to guide groups of teachers discovering what students, educators, and the public understands and how they are thinking. The tool, developed by Eric Buchovecky, is based in part on the work of the Leadership for Urban Mathematics Project and of the Assessment Communities of Teachers Project. The tool also draws on the work of Steve Seidel and Evangeline Harris-Stefanakis of Project Zero at Harvard University. The protocol gives a detailed step by step guide on how to prepare and conduct a healthy, productive conversation about and with use of data. It starts with a selection of datasets that do not lead to a single conclusion and generally lead to rich conversations. From that point forward, the protocol describes 6 stages the group has to follow with the help of a facilitator in...
Post a Comment
Read more

What Data Can’t Do

I came across this fascinating article from the The New Yorker that really speaks volumes about how careful we have to be when it comes to using and visualizing data. Whenever you try to force the real world to do something that can be counted, unintended consequences abound. The COVID pandemic demonstrated just how vulnerable the world can be when you don’t have good statistics, and the US Presidential election filled our newspapers with polls and projections, all meant to slake our thirst for insight. The same applies to education where knowing what to measure, but also why you want to measure it, is the primary hurdle to tackle. We all have a tendency to naturally trust data as it aims to represent something we are observing. However there are times when simply even solid data is not enough for decision making. That’s why the context, the aim, and the balance between quantitative and qualitative data is so important. As the article states: “The great psychologist Daniel Kahneman, w...
Post a Comment
Read more