What does it mean to visualise data anyway? How do we understand the world around us?
These are the kind of questions that have been the focus of my year 10 mathematics students lately, and one of the big issues to look at lately has been the spread of the Ebola virus. There are lots of fantastic visualisations of the data out there – but sometimes seeing the data as static is not quite enough to grasp what is going on. I am a big fan of running computer simulations to help my own understanding, but that does require a fair bit of expertise and time. So how to introduce a simulation into the classroom, without having to program? Introduce: The Ebola Game.
We took 3 statistics; that Ebola in West Africa was roughly killing 1 out of every 2 people infected, an infected person was passing on the virus to another 1.7 people on average, and that the virus’ symptoms were relatively low key in the first week and horrific in the second. I set up a turn based game, with each turn representing a week in the life of our small village. The students then set up the game rules:
- Each week they would “meet” 3 other people in the village, and that each meeting had a 50% chance of spreading the virus.
- If you contracted the virus, your first week was spent living a normal life in the village (possibly infecting others you meet), but the second week you had to go to hospital
- At the end of the second week you had a 50% chance of surviving. If you survived, you were then immune.
And so the game began, with yours truly secretly taking notes on who was currently infected without their knowledge. Students wandered around the class and had to shake hands with 3 different people, representing the three potential transmissions they were encountering each week. I acted as the media, reporting the new cases each week (along with the occasional suspicious flu just to keep them on their toes). To make it more interesting the students took on a few roles:
- Mayor (elected). They had the power to enforce a curfew, effectively reducing the number of enforced meetings between students per week.
- Doctor and Nurse. Both worked at the hospital, so were forced to interact with known cases, however the students decided that they would have a lower chance of catching the virus because they would be taking precautions.
- Undertaker. Escorted the ‘dead’ from the village, again with a small chance of catching the infection.
Things got interesting quickly. Students got quite reluctant to shake hands (understandably!) and started petitioning the Mayor for stronger restrictions. If someone went to hospital fear quickly spread amongst those that had had recent contact with the patient. No one went to visit anyone in hospital. Ever. The nurse got sick, but survived and was thus immune. The undertaker died so another student had to take up his job (reluctantly!). As the simulation went on we graphed the number of infections and the number of dead on the board, both steadily rising. However, it was not until the curfews and enough immunity spread through the group that the cases started to die out, but by that time a third of the class had perished.
It was a great trigger for conversation about what the statistics mean. I felt I learned a lot personally in that a simulation can be run in a low tech and much more entertaining way. The biggest gain here though, was really unpacking such a simple statistic such as “1 in 2 people” and seeing what this really means.
Koan Mathematics 