Lesson video

Hi, everybody.

I'm Mrs. Brookes.

And today's lesson is on data analysis and evaluation.

This means not only are we looking at data and how to present it, but also going a little bit further than that and looking how we would analyse that data, and in some cases, how we might evaluate that data.

Our outcome for today is, can we be able to analyse and evaluate data using various methods of data presentation? Our keywords, therefore, are analyse.

We are familiar with this word in other areas of the course, but today we just need to be aware that when we see analyse, we've gotta break up the components, the data, and we have to identify their characteristics.

We'll do more as that, as we move through the lesson.

When we see evaluate, we need to remember that J specifically, and that's when we're gonna judge from available evidence.

And it's really common that we'll use this word trend.

And when we see that, we just need to kind of be aware that a trend is when there's a direction in which something, often, the data, is developing or changing.

So there are two parts to the lesson.

The first one is where we're gonna look at analysis and we're gonna look at data that's presented in both a table and a line graph.

And then we're gonna develop that learning onto how to evaluate data.

And this time we're gonna look at data in either a bar or a pie chart.

So let's get started with the first part of the lesson.

We are going to use Lucas as our example here, and Lucas is a 17-year-old dancer.

And he is sharing with us here that he's been asked to analyse his heart rate data from a recent dance session.

So we are gonna help Lucas understand what that means.

First of all, we need to actually see that data, and you might want to pause the recording at this point to make a note of that data, and also use your previous learning around line graphs and how you would actually present that data correctly into a line graph.

We need to make sure that the line graph has a title, and also, that the axes are drawn correctly.

So we can see here we've got time along the horizontal axis and we've got heart rate along the vertical or the y-axes.

And in both cases, our units are clear and have been put in brackets.

Now when we then plot that heart rate, we can see here each different.

Sorry.

Each heart rate in that beats per minute has been plotted for every five minutes of that session.

And then those plots have been connected with a line.

But now, we need to kind of really start to look at how we would analyse that table, or more importantly, that line graph.

So remember our analyse description is where we would separate the information into components.

So to make this a little bit easier to understand, essentially, what that means is we're gonna examine each section of the data, and with that, there will be an interpretation.

We will be able to almost see what that data is potentially sharing with us.

We've also then got to identify their characteristics.

So to make that a little bit easier about the data, we might use our knowledge, our learning, to see if there's any patterns, if there's any relationships between that heart rate and the time, and also, if there's a trend being being displayed, whether that be if something has changed or has developed.

Oh, we actually start to look at that data, which is gonna have our first check for understanding.

Which of the following is not analysed when looking at data? Is it A, patterns, B, styles, C, trends, or D, relationships? I'll give you five seconds to decide.

Absolutely well done if you identify there that styles is something that we don't analyse, but we do look for patterns in the data.

We do look for trends, and if anything is changing or developing, and also look at some relationships.

So in this instance, we'll be looking at the relationship between heart rate at that particularly time of the dance session.

Another question for us here.

When analysing the data, is it true or false if the context of the scenario is important? What do you think? Yes, well done.

That absolutely is true.

And in this instance, that context, Lucas taking part in a sport such as dance, which we would reference as being quite medium, possibly high intensity, will really support the analysis when we're looking at those patterns and those trends to do with the heart rate.

So here is our graph, and let's do the kind of the separation into components.

Let's say what we are seeing.

So we're gonna examine and interpret that data.

It might be now that you want to have that graph shown on your bit of paper, and you can start to pick certain sections like I'm going to do now.

So let's start from the beginning and look at this first part of the data.

And actually, that's showing us that Lucas' resting heart rate is below 60 beats per minute.

As we move through it, we can see, as the session starts, that heart rate is starting to increase.

So in that first five minutes of the session, we see how that line is moving upwards.

As the session goes on, that line has increased even more so.

And we would probably use the word there rapid.

So we've got a real increase in that heart rate.

Now as the session is in full flow, if we wanna use that word, we do see that that heart rate is fluctuating.

So that word means that there's no particular trend other than it's going up and down.

And that word there is fluctuating.

We can see, though, that they're potentially working in that aerobic training zone.

And to finish with, as this session starts to end or come to a conclusion, we can see, again, that word rapid and that heart rate is starting to decrease as Lucas is moving into that recovery.

So we can see there how we've separated the data and we're saying exactly what we're seeing.

We're examining what that data is showing.

But to complete the analysis, we've then got to identify the characteristics.

So a good way of remembering that is, or we've shown the say what, but now we need to look at the say why.

So why is that data showing what it was showing us? So we're gonna break it up again into those sections.

Remember that resting heart rate was below 60 beats per minute.

That potentially suggests that Lucas has got what we call bradycardia, where that resting heart rate is slow.

So, brady, slow, cardio, heart.

And when it goes below 60 beats per per minute, we know that is a response to a long-term effective exercise.

Remember, there was that increase at the start, that rapid increase? Potentially, could that be what we know as the anticipatory rise or the fact there's that release of adrenaline because activity is about to start or has started? It could be that there was that pulse raising activity as part of the warmup to really prepare Lucas for his dance session.

Now we were told Lucas was 17.

So we can see here, we've got 220 minus 17 to work out his maximum heart rate.

That comes to 203 beats per minute.

When we work out 60% of that, we get 121.

And we knew that at 10 minutes, this heart rate zone was kind of starting to be the case.

And that means that those more intense dance activities have actually started.

So let's just review this.

We've looked at this phrase, bradycardia.

A similar phrase around heart rate linked to anticipatory rise.

We know our stages of the warm-up, and often, that stage one is that pulse raising activity.

And throughout our learning, we've done a lot of work around training zones and being able to calculate them.

So in this instance, working 60% of that maximum heart rate.

Let's carry on.

Just to begin with, we talked about that fluctuation in the middle and we then know that we are in that 80% of the maximum heart rate.

So we can see how that's increased slightly, 262.

Still using that 203 as that maximum heart rate.

And we're in that aerobic respiration, but the trend did show us there that those more intense periods, maybe, were where it spiked slightly, and those period of rest might be when there was a period of recovery or when the practise might have been more about listening and trying things out in a more steadier way before then doing it in a more intense way.

And finally, that decrease in heart rate suggests that definitely the session was coming to a conclusion, and the cool down, potentially, was staying elevated just to start that recovery process, but didn't need to be as high as what it was during that session.

So let's review this bit.

We've got our calculation again, but in this instance, 80% of that maximum heart rate.

We know around our aerobic and anaerobic respiration with oxygen or without.

And also some learning there around the cooldown and what that cooldown involves.

And as Lucas is going to share with us here, he can really see how that analysis is linked to topics that have been studied in that GCSE PE course.

So really using that knowledge to be able to examine the data, the say what, but more so than kind of say why that data is showing us what it is showing.

So now we've kind of practised that together.

Which of the following are associated with analysis of data? Do we, A, examine the data? Do we, B, use knowledge to identify trends? Do we, C, make a judgement ? Or D, weigh up the pros and cons.

What do you think? Yes, well done.

Absolutely.

We do two of those things.

We examine the data, the say what, and then we use the knowledge to identify the trends, the say why.

So, like Lucas, we're now gonna go onto our first task and this time we're going to be using Rio.

Now Rio is 19, and he's a triathlete.

And if we're not sure what a triathlon is, it has three distinct parts.

It has a swim at the start, then it has a cycle, and then it has a run.

And we can see here that the heart rate has been taken from the cycling section part too.

And what we'd like you to do is divide this into two parts.

The first part is plot the data on a line graph, and then secondly, practise that skill of analysis over some set distances.

So this is our data.

You can see it's in its table form, little bit like it was previously.

We've now got it in intervals, but our intervals are kilometres rather than time.

So we can see the heart rate has been taken at five-minute intervals and we can also see our correct units for our heart rate with BPM, meaning beats per minute.

So this is the data we use to plot our line graph.

And when we've done that, we'd like you to do some analysis in three sections.

So remember, we're gonna examine and interpret before we do the say why.

So we need you to look at the start, the period of time between five and 20 kilometres, and then what you notice happen at 25 kilometres.

Pause the recording and come back to me when you're ready.

Welcome back.

Quite a bit of a chunk of work to do there, but how did you do? On the first part, in terms of plotting the graph, have we got our axes labelled correctly? We can see now we've got that distance, but we still had our units of kilometre, likewise with the heart rate and with that BPM.

So our x and y-axis are labelled correctly.

The heart rate has been plotted, each of those five kilometres, and then join together with a line.

And we can see straight away, we've got a very different profile from what we had when we were looking at Lucas.

Not so much change, but there is some stuff that we can then examine and interpret, and then do the say why.

Now our checkpoint for that analysis is here.

We've looked at the data, we've presented it clearly, and now we're gonna look at those separate sections, see if there's any trends, and then use that knowledge.

So the trends is the say what, and then use that knowledge to be the say why in terms of what that pattern is actually showing.

And we asked you to look at three areas.

So what you could have said for the start of the cycle is that Rio was already in that aerobic zone.

So we can see that by working out his maximum heart rate, 19 taken away from 220.

When we work out 60% of that, now we get to 120.

6 beats per minute.

And that's probably 'cause there's that transitioning from already competing and doing that first part of that swim.

Now between five and 20 kilometres, the heart rate trend is very different than what we've looked at previously, and we actually see a plateau.

So a plateau is a word to kind of show when the heart rate almost goes on a straight line.

And in heart rate, in understanding heart rate, that's when we see a trend of oxygen supply is equivalent to oxygen demand.

So it means Rio's heart rate is not having to increase anymore because the amount that oxygen he's taking in is sufficient for what his muscles are requiring at that point.

In some cases, we call this steady state and it is linked to this plateau in the heart rate.

And within that, Rio is then working aerobically, and it would suggest that he can maintain that pace at that part of the race.

And the last bit of analysis was then what did we notice happen at 25 kilometres.

And we did see that spike as we refer to it or where it increased slightly.

And if we look into that into greater detail, he then moved into that anaerobic zone 'cause we know we can work that out by using 80% of the maximum heart rate.

And that would probably suggest that things have changed with regards to intensity in the race.

That could be that he's doing a hill climb or it could be that he's trying to overtake an opponent to try and get into a better position, but clearly doing something where he's working a little bit harder.

Which means we can now move on to the second part of the lesson where we follow a similar approach.

But now we're gonna look at that evaluation, and the data this time will be in a bar or a pie chart.

So we'll talk about Lucas again, but now talk about him and his troop.

And we're being told here that they did a vertical jump test.

So there's a little kind of GIF there to remind you of that protocol.

And what Lucas is sharing with us is that their results were compared to normative data, and then they presented their table of results in both a bar and a pie chart.

So let's just break that down to help us with our knowledge.

So Lucas scored 58 centimetres.

We can see here, we have that normative data for 15 to 16-year-olds.

What category is Lucas in? Absolutely.

We can see here.

We are looking in that male section of the data, therefore his category is above average.

So, like Lucas, now we have the whole troop.

We can see in this table, we've got their results in the vertical jump test, and then normative rating using that data on the previous slide.

Now this could be an opportunity for you to pause the recording, and you take those scores and put them into a bar chart or a pie chart, or both.

Now if you did it as a bar chart, I'm hoping it looks very similar to this.

We've got our title.

We have our names along the bottom.

And we have our scores with that unit along the vertical axis.

And like previous learning, we've got those columns that are equal in width as we present that data.

Now we're moving beyond that now with our skill, and as Lucas is sharing with us there, their dance teacher has asked them to evaluate this data.

So we're gonna help Lucas with that evaluation.

Remember, this is one of our keywords.

So we've got that J as a real trigger to remember that when we evaluate, we have to make a judgement.

We're looking almost a judgement on the effectiveness of that data.

So that will involve us giving some potential positives.

So what's good about the data, but also maybe looking at some negatives or some limitations of that data.

In some cases, part of the evaluation might be that we think there is a more suitable data range to display, and then, if so, why that is more suitable.

So, evaluation is very different to analysis.

So for our first check for understanding, is this true or false? Evaluate is a skill to present key points about the data and then justify.

Well done if you actually identified that as being false.

Remember, evaluation is where we examine the data, and then we make a judgement on its effectiveness, and that judgement will involve weighing up those positives and negatives.

So this was our bar chart for the dance troop which we've already seen.

Can we kind of look at some potential positives of that data? Now we're gonna have to use our knowledge again, just like we did with analysis.

So we do know the vertical jump test is a test of power, specifically in the legs.

So dancers really do need that to be able to jump and to be able to elevate.

So in this instance, that's the positive, that that data that it is sharing with us, the potential power in the dancer's legs.

What it also does is it kind of identifies those dancers that this may be a weakness for them.

So you can see, if we look at that specifically, Raheem and Corey are a lot lower than the rest of their troop.

And from that, a positive of that, is those dancers can be set a specific training programme to focus on their power in their legs, which might then help them with their elevation.

Now we've gotta do the opposite and we now need to look at those negatives of this data.

Hopefully, you were thinking, "Well, yes, dancers need to be powerful, but not just in their legs.

They need power in other areas of their body, maybe their arms, when they're lifting and pushing themselves from the floor." And our data doesn't show us that, it just shows us that power specifically in the legs.

And it's from a standing jump, which, for some dancers, they don't always do that.

They have to jump when they're on the move.

And that data could also be seen as quite demotivating for those dancers that did not score as well as others.

And as dancers, there are other areas of the sport other than power and fitness that they need to perhaps look into, and that data does not show us any of those kind of skill requirements that they need for their dance.

Now we also looked at their ratings.

So we can see here, we've now got their ratings as opposed to their scores.

So how many of them were in excellent, all the way down to poor.

And this can also be shown as a pie chart.

So we can see a real link there in terms of the number that were in each of those ratings.

Little bit of an opportunity for you to look at that pie chart in more detail and just answer this question here.

Which of the following is correct for the percentage of the dancers with an excellent rating? Well done.

We just needed to look really carefully at that pie chart and we would've identified that D is the correct answer.

So in that excellent rating, according to our pie chart, 37.

5% of the whole troop scored in excellent.

Well, could we now do some evaluation of this? Let's look at those positives.

That high percentage of dancers is good and there's only a small percentage that were average and below.

So that shares with us that their training and practise has been really beneficial.

Potentially means that as a troop, they have got that good elevation.

Now that data is not compared to other troops, though, and it only presents data from one fitness test.

So this is where our, is there anything more suitable, could come in as part of our judgement.

And it might be that we wanna stick with the jump test rather than giving a different one.

But we could then present or suggest, that if that jump test was done over a period of time, that would be a good way of looking at data over a number of weeks or a number of sessions to really kind of monitor that improvement, and that in itself will help with motivation.

So, back to Rio, and as our triathlete, we're now gonna look at Rio and his team, and that's a team of eight cyclists, and they've also completed a fitness test, but this time they're looking at their sit and reach test that they're doing at their training camp before their races.

So this is not during the race, it's part of their training camp.

Like we did in our first part of the lesson, we're gonna, first of all, plot the scores in a bar chart, and then the rating into a pie chart, like we did with Lucas.

And then we're gonna work on that skill of evaluation.

So here's our data.

We've got our eight triathletes with their sit and reach test and their normative rating.

Those ratings have been worked out from this data here.

So this is the correct data to look at those ratings.

And we are obviously looking at that male category, 'cause this was a team of males.

Pause the recording and come back to me when you guys are ready.

Welcome back.

How did we do? So in that first part, you may wanna pause the recording at this point and just check that your graphs are in line with what's on the screen here.

We've got those actual tests per athlete.

There was eight in total as a bar chart.

And then those ratings are there as a pie chart.

Like before, we're gonna look at a checklist for what we might do in order to evaluate this data.

We've looked at it.

We've presented it as the graphs.

Like before, we're gonna examine it and identify any of those trends.

But this time, we're using that knowledge to weigh up positives and negatives of that data, and therefore come to that judgement on its effectiveness.

Now what you might have said for those positives.

There was a high percentage who had an above average rating in that sit and reach test, and only two athletes were below average.

And that's a good thing because that's really showing that lower back and hamstrings are flexible.

And for those, if you've ever been on a bike, you'll understand that that's really supportive for that cycling technique and that position.

Now if those of the athletes that were below that rating, that could be showing that area of weakness and could be then addressed as a potential way of preventing injury and trying to enhance that flexibility.

However, the negatives, 'cause we've got to look at both sides.

We're not quite sure on that reliability of that data.

So if we were to do that consistently, would it give us the same results? It might have been, that as part of the training camp, those cyclists were already warm due to previous exercise, and therefore, in that test, they potentially may have reached further.

And it doesn't compare to other competitors.

And maybe a more suitable test for our cyclists is to do something around VO2 max or predicting a VO2 max.

So well done on working on our analysis and our evaluation.

To summarise, we've learned that data can be provided in various methods, whether that's a table, a line graph, a bar chart, or a pie chart.

And when that is presented in one of those formats, it can then be put into another, if that's gonna help us with our analysis.

So we did that frequently from the table into one of those graphs or charts 'cause that helped us look at those trends.

Once that is presented, remember, the analysis is where you examine and separate the data, and identify the characteristics.

So we use the rhyme, say what, say why.

And from that say why, you will use that previous knowledge to look at those trends, patterns, potential relationships.

With that evaluation, it's a really similar process to analysis.

However, you are coming to then a judgement on that effectiveness of that data.

And again, you're gonna have to use the knowledge to weigh up those positives and negatives.

Thank you so much for joining me on today's lesson and I look forward to seeing you on the next one.