I saw David McCandless’ TEDTalk right after it came out and it still remains one of the few I like to show to students. The dispositions to interpret patterns in big data is, for me, one of the ‘new’ skills that students didn’t really need it when you and I went to school.
Data is the new (s)oil.” – David McCandless
McCandless speaks of the quote above in the context of big data and its potential to unveil consumer and behavioral patterns. Schools followed suit, and WIDA, MAP, and other forms of assessments that collected longitudinal data on student improvement are almost ubiquitous.
First ideas on the use of data visualization
I first ventured into the world of data visualization when Art Costa came to the American School of Rio de Janeiro in 2007 to deliver PD on the Habits of Mind. The idea was to have a common language to use when reporting on student performance and progress – similarly to what happens with the IB Learner Profile. I created a one-page document for each week of classes where I could collect data on individual students that would be graphed every 2 weeks to show the patterns (per class and per student).
When I moved to Serbia and started working in the MYP/DP, I did something similar for the Learner Profile. Instead of documenting it myself, I tried having students self-report using an arbitrary scale. Psychometrics was the roadblock I didn’t know how to overcome, and I thought the results were too unreliable to carry any weight. I actually presented on this at the CEESA Conference in Budapest in 2011. However, I still use it in workshops as a provocation on the role of the Learner Profile in IB classes.
Consolidated classroom practices that use data visualization
In Grade 10 Biology at NIS, students interpret data from a scientific article each semester and present their findings. Students need lots of support on the task because the articles are only loosely connected to the content of the unit. I introduce the assessment by visiting the library and taking out the box of previous issues of Scientific American and take turns interpreting the infographics in the Graphic Science section. Ultimately I replaced infographics with charts from scientific papers because there was more room for student choice and differentiation.
The Knowledge Framework is a tool used in Theory of Knowledge classes to analyze and compare different Areas of Knowledge. It allows students to examine how knowledge is produced, communicated, and evaluated, both within and across disciplines.
In my classes, small groups of students select one of the Areas of Knowledge to produce infographics. A key point McCandless makes is that concepts can be visualized beautifully just as well as numbers. In that sense, my students’ work is similar to his comparison McCandless makes between political parties. They’re different in that students only examine one Area of Knowledge, so the purpose of the infographic is different.
Last week, during a Zoom call with Chris Jensen as part of the EARCOS Leadership Mentoring program, I was introduced the Leadership Circle 360. This is an inventory that collects data from the leader and colleagues to create a radar graph evaluating different leadership attributes, including reactive profiles (sometimes called “shadow skills”). I am hoping to take this assessment, at which point I’ll revisit this post and see how my prediction went.