by Jenna Laib
Last week, a middle school social studies teacher and I co-planned a slow reveal graphs lesson to launch a project about current events and the undercurrent of contributing factors. The class had read two different articles about Iran (one about the Iranians protesting and one about the Iranian team in the World Cup). We wanted to continue talking about protests in Iran, but to use it as a way to discuss protest in general. And I wanted to use a slow reveal graph to show how compelling it can be to engage with social studies through data.
Planning for difficult conversations
This is a challenging topic. In general, the students don’t know a lot about the historical context for the current protests in Iran, and there was potential for the discourse to develop shades of Islamophobia quickly. It’s easy to unintentionally reinforce ideas “white saviorism,” white supremacy, and Western colonialism. Thus, I spent most of my time planning how to navigate some of these thorny issues during discussion. In particular, I worried about the discussion of hijab and women’s rights. I am a practicing Muslim woman who sometimes wears hijab when I teach. No teacher is never “neutral” when we teach — we bring our identities to the classroom — and this felt particularly symbolic. I don’t believe that women should be forced to wear hijab, and I’m just as fervently against hijab bans in countries like France.
And the issues become symbolic. The protests aren’t strictly about the death of Mahsa Amini, nor are they strictly about hijab, although both are large (and devastating) issues. There are many hidden factors, beneath the surface like an iceberg.
…which is all to say that I thought about the graph we chose, and put considerably thought into the order of the reveal to tell a particular story, but I may have taken a few things for granted.
Use the arrows to navigate through the reveals.
Making sense of the x-axis: time
The second slide reveals the x-axis: time, in hours. Most students (American-born) recognized this as “military time,” although it’s frequently used around the world.
I asked students, “how does this change your thinking about the graph?”
In each of the 8th grade sections, there was a discussion about the dramatic dip in the data. It starts to fall before 16:00, and then seems to hit a low point, where it plateaus, around 16:30. What is that, 4:30? What might be happening? Students offered up some plausible explanations.
People are leaving work or school.
It’s getting dark.
These all seemed fairly reasonable, although, of course, I knew that this graph measured internet bandwidth and traffic in Iran, and that the internet would have been turned off as protests were starting to gain traction. I thought things would accelerate as it grew darker, and the youth took the streets, and so while 4:30pm in September did not sound like it would be particularly dark, I assumed I had been mistaken.
A Tiny Detail is Revealed
Much later in the process of slow revealing, the time is revealed. There are some tiny details in there, like how the data for the graph starts at 1:09 instead of the assumed midnight, on September 21. It also mentions the time zone: UTC.
Wait. UTC. That doesn’t sound right for Iran. Isn’t that…? I googled it quickly during the first class of the day.
UTC is Coordinated Universal Time. It’s not a time zone at all, it’s a standard for measuring time. Iran’s time zone is Iran Standard Time, which is GMT+3:30.
16:30 on this graph isn’t 4:30 local time at all. In fact, it was +3:30, making it 20:00 or 8:00pm local time. It would be dark outside. It would be after dinner, perhaps after evening prayers, and this would change how we imagine the experience of the Iranian citizens.
“Oh! Okay! So! It turns out that the data for the graph is mapped out using UTC, but the local time for the place where the data was gathered is not UTC,” I told students. I could see confusion forming like actual clouds above their heads.
Brilliantly, social studies teacher Kevin Oliveira pulled up a map of time zones via Wikipedia. So, if we’re talking about a country that is GMT +3:30, what…
There is only one country that seems to exist between GMT +3 and GMT +4: Iran, seen here in blue and green stripes.
“So how does this new information change our understanding of the graph? “
Using slow reveal graphs to dig deep
Students were deeply empathetic. Nobody likes to lose their internet connection. In our Northeastern corner of the US, we have experienced this for mostly unplanned reasons, like major storms and internet provider issues. It’s an entirely different level of frustration to experience this for deliberate reasons from a controlling government. This would cause tremendous issues with both external communication — letting the world know what’s happening! — and internal communication, e.g. organizing protests, communicating how to stay safe, and letting family know about personal safety. We discussed the role of social media in both internal and external communications. We read parts of an article that discussed how most Iranian youth use mobile apps like Whatsapp to communicate, which avoids texting charges, and that this mobile outage would greatly impact any ability to converse.
But, most importantly, we were able to anchor this conversation both on texts and on data.
Using the slow reveal graph allowed us to bring in student knowledge about both the mathematics and the context. It pushed us to look closely at the details of the graph, and to make sense of the data in new and different ways. If we had started with the full and complete graph, I imagine students would still notice the dramatic drop. (It’s hard to miss!) I don’t know if they would have had enough time to build up that empathetic lens around how this would impact people, or experience that same sense of awe and wonder when we revealed that this graph was about internet connectivity.
And I don’t think we would have realized anything about the time zones. We needed that space to dig into the data, notice closely, and let everything breathe. As more layers are added, the conversation shifts. All of a sudden, it’s reasonable to talk about one graph for half an hour.
Most importantly, using the slow reveal routine creates opportunities to become critical consumers of data. What exactly do these numbers mean? Why might the data be shown this way? How do these little nuances that we observe change how we understand the story of this data?