Data visualisation: Languages of my students and data flaws

The inspiration for this week’s data collection came from a chapter on multilingualism I read in ‚The Infrastructure of Accountability: Data use and the transformation of American education’ by Anagnostopoulos et al. (2013). German, French, Italian and Romansh are the four national languages, and multilingualism is close-knit with Switzerland’s history and culture. In the past decades, many more non-national languages became widely spoken, especially due to immigration. 

In primary or secondary school, all children must learn at least one other national language. The school I am working in uses web-based software, where all data of our students is stored and can be accessed by all staff. This week I had coaching conversations with my students to reflect on their learning and to set new goals for the current term. I have taken this opportunity to discuss their language background with them, which I often do, since I am their language teacher.

I converted my notes in the data visualisation for this week:

Each black line represents a student. 

Starting at the inner circle, the marking dots illustrate:

First dot:. the place of origin/town of citizenship  Second dot: the first language/mother tongue Third dot: the languages spoken at home

a green marking dot = recorded data in the software is correct a blue marking dot = recorded data in the software is incomplete a red marking dot = recorded data in the software is false

As you can see from the visualisation, I discovered wrong or incomplete data in all areas. The place of origin is quite a complex topic itself (read more on this here:, with Switzerland IDs never showing the place of birth, but always the place of origin. All my students were born in Switzerland, yet they often have a foreign place of origin (because their parents or grandparents immigrated). This is highly questionable since this data in the software is used to “fabricate a narrative of performance” (Williamson, 2017, p.82), especially because funding depends on the percentage of students with a migration background. “Data technologies of various kinds are policy instruments of performativity and accountability”(Williamson, 2017, p.76) and if this data is flawed or imprecise in the first place then the consequences are massive.

The same line of thought applies to the areas of their first language and the language they speak at home. Of course, this short analysis is not representative, yet I am only beginning to understand how important data literacy is in our profession: This importance will only increase over the next years. 


Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (2013).  The Infrastructure of Accountability: Data use and the transformation of American education. 

Williamson, B. (2017). Big Data in Education: The digital future of learning, policy and practice. Sage.

One Reply to “Data visualisation: Languages of my students and data flaws”

  1. This is really fascinating, and shares some similarities with the post by Matt Offord this week (he was looking at the collection of diversity data). One of the things you might both want to check out is work by Evelyn Ruppert on censuses, where she explores “how the interests and political influence of various actors shape census making. The census is thus understood as a particular way of defining, collecting and organising social observations about individuals and not a simple reflection of an empirically existing reality” ( I feel there is something in that work that resonates with your reflections here. In terms of an example of how “place of origin” can be used for questionable governing purposes, in the UK the government’s National Pupil Database used to collect that data, which were then used for immigration enforcement. Only very recently, that was judged to be illegal and has now been stopped ( So collection of this kind of data in education can have truly far-reaching consequences, as you’ve noted. I look forward to your final reflections next time.

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