Learning how to ask for and give feedback in English is an important functional skill for my students in Istanbul. In this lesson I combine reading, listening, and speaking activities to guide the student to connect feedback to self-improvement. In her book Daring Greatly, Brene Brown emphasizes the importance of feedback to cultivate change and engagement. "A daring greatly culture is a culture of honest, constructive, and engaged feedback," she writes. "Without feedback there can be no transformative change" (197). Asking for and giving feedback becomes essential to constantly improve.
After reading this passage, I ask my student A., a surgeon, to compare and contrast Formula 1 racing and medical care. He calls this an 'attractive topic,' and quickly lists the differences and similarities. Then, we discuss the importance of feedback to both the racecar driver and the surgeon. Next, we listen and fill in the vocabulary gaps. We pause sometimes to compare the American and British pronunciation. Finally, we give Peter van Manen, the TED presenter, some feedback: he gives a dynamic presentation but does not really convince the listener that the ambulance could be an extra hospital bed or that the medical team could respond any faster even with the real time data available. In addition, this TED talk gives A. some ideas of how to create more dynamic presentations. In his upcoming presentations in Turkey, he plans to ask more questions to engage the audience. Here is the lesson:
(1) Compare and contrast Formula 1 racing and medical care:
- Formula 1 racing has a racecar with an engine, chassis, electronics, and tires; a racecar driver; a racing team.
- Medical care has an ambulance with a bed; a patient with health conditions; a medical team of doctors, nurses, and surgeons.
- Both Formula 1 racing and medical care monitor and use telemetry to collect, store, and send data about the changing the physical conditions of the racecar and the patient, respectively. Both seek constant, immediate feedback of the specific changes in health.
)
Listen for numbers and fill the gaps.
0:11 Motor racing is a funny old business. We make a new car every year, and then we spend the rest of the season trying to understand what it is we've built to make it better, to make it faster. And then the next year, we start again.
0:27 Now, the car you see in front of you is quite complicated. The chassis is made up of about ________components, the engine another ________, the electronics about ________. So there's about ________things there that can go wrong. So motor racing is very much about attention to detail.
0:50 The other thing about Formula 1 in particular is we're always changing the car. We're always trying to make it faster. So every two weeks, we will be making about ________new components to fit to the car. ________to ________percent of the race car will be different every two weeks of the year.
1:10 So how do we do that? Well, we start our life with the racing car. We have a lot of sensors on the car to measure things. On the race car in front of you here there are about ________sensors when it goes into a race. It's measuring all sorts of things around the car. That data is logged. We're logging about ________different parameters within the data systems, about ________health parameters and events to say when things are not working the way they should do, and we're sending that data back to the garage using telemetry at a rate of ________to ________megabits per second. So during a two-hour race, each car will be sending ________ million numbers. That's twice as many numbers as words that each of us speaks in a lifetime. It's a huge amount of data.
(3) Listen and fill the gaps with these vocabulary:
- amplify
- better
- catastrophic
- connectivity
- cues
- detect
- deteriorate
- deteriorate
- determine
- early warning
- fuzzy
- go bad
- go wrong
- indication
- install
- interpretation
- look at
- monitor
- patterns
- predictable
- rely
- see coming
- specific
- store
- stream
- tease out
- undetected
- unpredictable
- unusual
2:28 Okay, so let's have a look at a little bit of data. Let's pick a bit of data from another three-month-old patient. This is a child, and what you're seeing here is real data, and on the far right-hand side, where everything starts getting a little bit ________, that is the patient going into cardiac arrest. It was deemed to be an ________event. This was a heart attack that no one could ________. But when we look at the information there, we can see that things are starting to become a little ________about five minutes or so before the cardiac arrest. We can see small changes in things like the heart rate moving. These were all ________by normal thresholds which would be applied to data. So the question is, why couldn't we see it? Was this a ________event? Can we look more at the patterns in the data to be able to do things ________?
3:26 So this is a child, about the same age as the racing car on stage, three months old. It's a patient with a heart problem. Now, when you look at some of the data on the screen above, things like heart rate, pulse, oxygen, respiration rates, they're all ________for a normal child, but they're quite normal for the child there, and so one of the challenges you have in health care is, how can I look at the patient in front of me, have something which is ________for her, and be able to ________when things start to change, when things start to ________? Because like a racing car, any patient, when things start to ________, you have a short time to make a difference.
4:13 So what we did is we took a data system which we run every two weeks of the year in Formula 1 and we ________it on the hospital computers at Birmingham Children's Hospital. We ________data from the bedside instruments in their pediatric intensive care so that we could both ________the data in real time and, more importantly, to ________the data so that we could start to learn from it. And then, we applied an application on top which would allow us to ________________the patterns in the data in real time so we could see what was happening, so we could ________when things started to change.
4:53 Now, in motor racing, we're all a little bit ambitious, audacious, a little bit arrogant sometimes, so we decided we would also look at the children as they were being transported to intensive care. Why should we wait until they arrived in the hospital before we started to look? And so we installed a real-time link between the ambulance and the hospital, just using normal 3G telephony to send that data so that the ambulance became an extra bed in intensive care.
5:25 And then we started looking at the data. So the wiggly lines at the top, all the colors, this is the normal sort of data you would see on a ________-- heart rate, pulse, oxygen within the blood, and respiration. The lines on the bottom, the blue and the red, these are the interesting ones. The red line is showing an automated version of the ________________score that Birmingham Children's Hospital were already running. They'd been running that since 2008, and already have stopped cardiac arrests and distress within the hospital. The blue line is an ________of when ________start to change, and immediately, before we even started putting in clinical ________, we can see that the data is speaking to us. It's telling us that something is ________.
6:15 The plot with the red and the green blobs, this is plotting different components of the data against each other. The green is us learning what is normal for that child. We call it the cloud of normality. And when things start to change, when conditions start to ________, we move into the red line. There's no rocket science here. It is displaying data that exists already in a different way, to ________it, to provide ________to the doctors, to the nurses, so they can see what's happening. In the same way that a good racing driver _______on_________to decide when to apply the brakes, when to turn into a corner, we need to help our physicians and our nurses to see when things are starting to ________________.
7:05 So we have a very ambitious program. We think that the race is on to do something differently. We are thinking big. It's the right thing to do. We have an approach which, if it's successful, there's no reason why it should stay within a hospital. It can go beyond the walls. With wireless ________these days, there is no reason why patients, doctors and nurses always have to be in the same place at the same time. And meanwhile, we'll take our little three-month-old baby, keep taking it to the track, keeping it safe, and making it faster and better.