Hello, and welcome to today's lesson from the unit, Photosynthesis Factors Affecting the Rate.

I'm Mr. Jarvis, and it's good to be with you today.

I'm gonna be taking you through today's lesson, which is all about the effect of light intensity on the rate of photosynthesis in pondweed.

And today, we're evaluating our experiment.

By the end of today's lesson, you should be able to evaluate and explain data from an investigation into the effect of light intensity on the rate of photosynthesis in pondweed.

There are five key words to today's lesson.

They're on the screen now, along with their definitions.

The key words are; accuracy, error, precision, repeatability, and reproducibility.

You can pause the video if you want some time to read through the definitions, but we will go through them as we move through today's lesson.

Today's lesson is broken down into two parts.

First, we're going to interpret the data from the experiment, and then in the second part of the lesson, we're going to evaluate the experiment.

So if you're ready, let's get started with our first section, which is interpreting the data from our experiment on the effect of light intensity on the rate of photosynthesis in pondweed.

Photosynthesis happens in the cells of plants and other producers when it's light.

Light from the sun or from artificial sources is absorbed by chlorophyll in the leaves and the other green parts of plant.

The light transfers the energy needed for the reactions of photosynthesis to take place.

The reactants and products of photosynthesis are water and carbon dioxide.

The reactants, the molecules that go into the reaction, and glucose, the food, and oxygen, which are the products of photosynthesis.

The rate of a reaction is a measure of how much change occurs.

For example, how much product is formed per unit time.

The rate of photosynthesis depends on a number of limiting factors, and a limiting factor is a condition that when in short supply slows down or limits the rate of a reaction.

Light intensity, that's the amount of light that reaches a given surface area in a period of time, is one of the main limiting factors in photosynthesis.

Bright light allows plants to photosynthesize at a fast rate.

When the light intensity is much lower, for example, in the evening, or in the early morning, or even at night, photosynthesis occurs at a very slow rate, or even not at all.

Here's a check.

Which statement is an example of the rate of a reaction? Is it A-how long a chemical reaction takes, B-how much reactant is converted into product, or C-how much product is formed per unit of time? I'll pause for a few seconds, and then I'll check your answer.

The correct answer is C.

<v ->An example of a rate of reaction is how much product</v> is formed in a unit of time.

Well done if you got that right.

As light intensity increases, the rate of photosynthesis increases too until the rate is limited by other limiting factors, and this is shown on the graph on the screen.

At night, there is no light and photosynthesis does not take place, and we can see that at the point where light intensity is at zero, then the rate of photosynthesis is zero too.

Reactions including photosynthesis take place more quickly at warmer temperatures.

Here's a check.

When is the rate of photosynthesis likely to be fastest, A-on a warm, bright, sunny day, B-in the middle of the night, C-on a cool day with lots of cloud cover, or D-on a bright but snowy day in winter? I'll pause for a few seconds, and then we'll check your answer.

The correct answer here is A.

A warm, bright sunny day gives the right conditions for the rate of photosynthesis to be at its fastest.

Well done if you got that correct.

Before carrying out the experiment, you made a hypothesis.

And a hypothesis is an idea, for example, an idea about an outcome of an experiment based on observations and scientific understanding.

It should suggest why the outcome will happen using your scientific understanding.

And for an example, my hypothesis for the experiment was, as light intensity increases, the rate of photosynthesis in pondweed will increase as the light provides the energy needed for the chemical reactions of photosynthesis to take place.

We're going to analyse the data from the experiment and decide whether it increases or decreases your confidence in the hypothesis.

You should use your own data where possible.

I'm going to give you some examples using my exemplar data.

So here's my data, and we can see we've recorded the number of bubbles of gas produced in three minutes.

We've recorded the volume of gas produced in three minutes in cubic centimetres.

And we've repeated the experiment three times at each distance from the light source.

At a hundred centimetres from the light source, the light intensity will be lower.

And at 20 centimetres from the light source, the light intensity will be much higher.

The first way that we're going to analyse our data is by calculating some mean values.

We're going to start by calculating the mean number of bubbles of gas produced in three minute periods at each distance from the light source.

To calculate the mean, we need to sum the samples and divide by the number of samples.

So let's work through the first example at a hundred centimetres from the light source.

We have three readings, 28 bubbles, two bubbles, and 26.

If we add those together, 28 plus two plus 26, it gives us 56 bubbles.

There were three samples, so 56 divided by three will give me the mean number of bubbles produced in three minutes at 100 centimetres, and that's 18.

67.

We're going to record that in our results table.

I'm then going to calculate the mean values for 80 centimetres, which is 68.

At 60 centimetres, 83.

At 40 centimetres, 110.

33.

And finally, the brightest or most intense light intensity at 20 centimetres, 184.

33.

We're now going to analyse the volume of gas produced in three minute periods, and we're again going to calculate the mean volume of gas produced.

So a hundred centimetres from the light source, it's 0.

133 cubic centimetres.

At 80, it's 0.

233.

At 60 centimetres, it's 0.

433.

At 40 centimetres, it's 0.

733.

And at 20 centimetres, the brightest light or the highest light intensity is 1.

267 cubic centimetres of gas produced in three minutes.

We can plot both of these sets of results on graphs.

So here's our first graph.

We're going to plot the mean number of bubbles per three minutes against the distance from the light.

The distance from the light or the light intensity is the independent variable, that goes across the bottom of our graph, along the X axis.

The dependent variable, the variable that we measured, the mean number of bubbles per three minutes, goes along the side axis of the graph, the Y axis.

And you can see that I have plotted a scale along the Y axis that gives me the mean number of bubbles produced every three minutes.

We're then going to plot the results.

So at a hundred centimetres from the light source, there were 18.

7 bubbles produced in three minutes.

At 80 centimetres it was 68.

At 60 centimetres it was 83.

At 40 centimetres it was 110.

3.

And at 20 centimetres it was 184.

3.

Now, normally I'd draw a line of best fit, but at the moment there doesn't seem to be a clear pattern to this data, so I'm not going to do that at this moment in time.

Let's look at the mean volume of gas produced in three minutes.

And again, we've got similar axis, where we've plotted our independent variable, the light intensity, the distance from the light along the bottom, the X axis, and the mean volume of gas produced in three minutes along the Y axis.

At a hundred centimetres from the light, the gas produced was 0.

13 cubic centimetres.

At 80, it was 0.

23.

At 60, 0.

43.

At 40, 0.

73.

And at 20, the highest light intensity, 1.

27.

This time we do seem to have a trend or a pattern in our graph, and so I'm going to draw a line of best fit.

The rate of a reaction is equal to the amount of product produced per unit time.

And we can calculate the rate of photosynthesis using the equation rate is equal to the volume of gas divided by the time in minutes.

The sample results we obtained were after three minutes, so the time is three minutes.

Here are the results that we obtained from the mean volume of gas readings.

And you can see we've got the distance from the light source, and the mean volume of gas produced that we saw on the previous couple of slides.

To calculate the rate, let's use the 20 centimetre distance from the light source as our example.

So we have a volume of gas that's produced 1.

27 cubic centimetres.

And the time that it was produced over, which was three minutes.

So 1.

27 divided by three gives us 0.

423 as our rate.

That's 0.

423 cubic centimetres of gas produced every minute.

We can carry out the same calculation for the other distances from the light source, and here is the rate that I've calculated using my exemplar data.

The rate of photosynthesis can also be plotted as a graph, and this time we have the distance from the light along the X axis because that's our independent variable.

The variable that we're measuring is the rate of photosynthesis, and that goes along our Y axis.

And you can see again, I've plotted on my axis carefully to go up in equal units.

So let's plot our results.

I'm going to just put them on the graph for you.

And again, we can see there is a smooth line of best fit, which fits that data set neatly and tidely.

Here's a check, which graph is drawn correctly? And secondly, what's incorrect about the incorrect graph? I'll pause for a few seconds, and then we'll check your answer.

So graph B is the graph that's correct.

Graph A is not correct because we've got the axis labelled incorrectly.

The distance from the light source is along the Y axis.

And as it's the independent variable, that should be along the X axis.

And the dependent variable is plotted along the X axis when it should have been plotted along the Y axis.

And secondly, the scale on the X axis doesn't go up in equal units.

Well done if you spotted those errors.

That brings us to our first task.

First of all, using your data or the data provided, calculate the mean number of bubbles and the mean volume of gas produced in three minutes.

Calculate the rate of photosynthesis using your data, and add these results to your results table.

And then plot a graph of rate of photosynthesis against light intensity.

And draw a smooth line of best fit.

To be successful with your graph, remember that your axis need to be correctly plotted and labelled.

Your points need to be plotted correctly and accurately, and there needs to be a smooth line of best fit drawn.

And then finally, consider your hypothesis, or use the exemplar hypothesis that's provided.

Do your results increase or decrease the confidence in your hypothesis? You'll need to pause the video at this point, write down your answers, and draw your graph.

And then when you're ready, press play and we'll check to see how well you've done.

Good luck.

In terms of the first task, you needed to calculate the mean and the rate of photosynthesis for your data, and add that data to your results table.

Your results table should now look similar to the one that's on the screen.

And you can see I've added the mean bubbles per three minutes, the mean volume of gas in cubic centimetres in three minutes.

And I've added the rate, which is the volume of gas produced divided by the time.

All that data is now in my table.

Then you should have plotted a graph of the rate of photosynthesis against light intensity, and draw a smooth line of best fit.

Remember, your graph should have a axis that are correctly plotted and labelled.

And you can see I've got my independent variable along the X axis, my dependent variable along the Y axis, and I've got a scale that goes up in equal units.

Then I should plot my points accurately.

And I've used my data to plot my points on the graph.

And then finally, I've drawn a smooth line of their best fit.

Finally, you are asked to consider your hypothesis.

Do your results increase or decrease your confidence? So in terms of my sample data, your answer might have included that, "My results support my hypothesis.

They increase my confidence in the hypothesis.

And that's because the rate of photosynthesis increases as the pondweed is placed closer to the light source.

The closer the light source, the higher the intensity.

That's the amount of light reaching the pondweed in a given time." As the pondweed is placed further away from the light source, the light intensity decreases, and that correlates with the rate of decrease in photosynthesis.

Though there's a reduced number of bubbles produced, or reduced volume of gas produced in a three minute time period.

And that's because light is needed to provide the energy required for the reactions of photosynthesis to take place.

When light intensity is higher, there's more energy provided and the reactions take place more quickly.

Hopefully you got something along those lines in terms of justifying why your hypothesis was increased or decreased as a result of the experiment.

That brings us to the second part of today's lesson, and we're now going to evaluate the experiment.

So if you're ready, let's carry on.

Light intensity is the amount of light reaching a given surface area in a period of time.

The closer the pondweed is to the light source, the higher the light intensity.

As the distance increases, the light is spread over a larger area.

And there's a image on the screen that I'm going to use to show you this.

So at the highest light intensity, the light doesn't spread out very far, so there's more light reaching the surface of the pondweed.

As we start to move the beaker with the pondweed further away from the light source, the light starts to spread out, and so the light intensity reduces.

And at the furthest distance away, the light spreads out even further, so there's less light reaching the surface area of the pondweed, and therefore the light intensity is at its slowest.

The graph shows that as light intensity decreased, the rate of photosynthesis decreased too.

At highlight intensity there was a high rate, and at low light intensity there was a lower rate.

We know that light is needed to transfer the energy for the reactions of photosynthesis to take place, and so our graph makes sense.

Here's a check.

Whose explanation of the effect of light intensity on the rate of photosynthesis is correct, and who is incorrect? Laura says, "As the light intensity increases, the rate of photosynthesis increases.

Light transfers the energy needed for the reactions to take place." And Izzy says, "As light intensity increases, the rate of photosynthesis increases.

This is demonstrated by a reduction in bubbles produced by pondweed." I'll pause for a few seconds.

You need to decide which one of the girls are correct, and which are incorrect.

So let's take Laura first.

Laura is correct.

She said, "As light intensity increases, the rate of photosynthesis increases because light transfers the energy needed for the reactions to take place.

Izzy is incorrect, because she said, "As light intensity increases, the rate of photosynthesis increases.

And it's demonstrated by a reduction in bubbles produced by pondweed." If the rate of photosynthesis had increased, there would be an increase in the number of bubbles produced by the pondweed.

So Izzy is incorrect.

We can conclude that the rate of photosynthesis is increased as light intensity increases.

And we know this because the volume of gas, of oxygen produced, and the number of bubbles of oxygen produced both increased as the pondweed was moved nearer to the lamp.

Oxygen is a waste product of photosynthesis.

How could you test the gas to show that it was oxygen? I'll pause for a few seconds to get you to think.

So we could test for oxygen by putting a glowing spill into the measuring cylinder.

And if it was oxygen, it would reignite.

Well done if you thought of that.

We measured the rate of photosynthesis in two ways.

We measured the number of bubbles produced in three minutes, and the volume of gas produced in three minutes.

And we did that by recording the start volume in the measuring cylinder and the end volume in the measuring cylinder, and calculating the volume of gas produced in three minutes.

Which method was best? Why? I'll pause for a few seconds while you think of your answer.

By measuring the volume of gas produced using a measuring cylinder, we're measuring the actual volume of a product to photosynthesis.

That's oxygen.

We're measuring the rate of photosynthesis with more accuracy, and accuracy is when something is close to the true value.

The problem with counting the bubbles is that they are different sizes, and that introduces error into the results.

An error is a measurement that's different to what it would otherwise be.

IE, the true value.

The graph of the number of bubbles produced shows that there was a mean value that perhaps didn't match the line of best fit.

If you remember earlier in the lesson, I said it was really tricky to draw a line of best fit through the points because there didn't seem to be a clear pattern.

We know that the shape of the graph should be a curve as we've got the line of best fit here.

And you can see that the first four points in the graph fit the line reasonably well, but the fifth point at a hundred centimetres, it doesn't.

It's quite a way off our line of best fit, and I've circled that on the graph.

Can you think of any other sources of error in your experiments? Errors can be random errors.

That's when the size of the error is different for each measurement.

And one example here is the size of bubbles produced by a plant.

The other type of error is a systematic error.

This is when measurements differ from the true value by the same amount each time, and this can be caused by the environment, the method of observation, or the equipment used.

For example, a systematic error in this experiment might be from additional light from the surroundings affecting the rate of photosynthesis.

Let's move on to a check.

A pupil counted bubbles of oxygen given off by pondweed in five minutes.

There were four bubbles in the first experiment and 24 bubbles in the second experiment.

What might explain these results, A-the pupil moved the pondweed further away from the light source in the second experiment, B-there was a systematic error caused by the pupil miscounting the bubbles in the second experiment, or C-there was a random error caused by the size of gas bubbles produced? I'll pause for a few seconds, and then we'll check your answer.

The correct answer is C.

There was probably a random error caused by the size of the gas bubbles produced, which caused the difference in the results that were obtained by the pupil.

Well done if you got that right.

A lack of precision can indicate errors, and precision is the spread of results around the mean value.

Let's look at our results.

Here's our number of bubbles of gas produced in three minutes.

And we can see that there are some precise results.

The 80 centimetre set of results show 72, 66 and 66.

The spread of results around the mean value, 68 bubbles per three minutes, is really small.

In comparison, at 40 centimetres, there is a large spread around the mean value.

We've got a mean value of 110, but the data ranges from 88 as a minimum to 139 as a maximum.

When measurements are similar, when using the same equipment and method the experiment has repeatability.

When the volume of gas was measured, most results were similar or repeatable.

We can see this on the table.

How repeatable were your own results? Let's move to a check.

What's meant by the term repeatability, A-using the same method to do an experiment over and over again, B-getting similar results each time you do an experiment using the same method, or C-repeating an experiment until you get the results that are the same? I'll pause for a few seconds, and then we'll check the answer.

So repeatability means getting similar results each time you do an experiment using the same method.

Well done if you got that right.

By evaluating data, we can suggest ways that an experiment could be changed to improve the quality of the data.

When an investigation is repeated by another person, or a different method or equipment is used and the same results are obtained, the experiment has reproducibility.

Both of our methods had reproducibility because they gave us similar results.

How reproducible were your class results? The most reliable results come from experiments that have both repeatability and reproducibility.

Remember, repeatability is when you carry out the experiment using the same method and you get similar results each time.

And reproducibility is when someone else carries out the experiment using the same or a different method, and they get similar results or the same results.

Here's a final check.

Lots of scientists carry out investigations on the rate of photosynthesis in plants.

The data from experiments are similar.

This is an example of, A-accuracy, B-precision, C-repeatability, or D-reproducibility? I'll pause for a few seconds, and then we'll check the answer.

So lots of scientists carrying out investigations into the rates of photosynthesis in plants with the data being similar is an example of reproducibility.

Well done if you got that right.

Let's move to our final task.

You've investigated the effect of light intensity on the rate of photosynthesis.

I'd like you to suggest how you might change the experiment to improve the accuracy, precision, reliability, or reproducibility of the results.

Think about both the equipment and the method that you used.

Explain how each change will improve the results.

You'll need to pause the video to write down your answer, and when you're ready, come back and I'll give you some example responses that you might have had.

So I asked you to suggest how you might change your experiment to improve the accuracy, precision, repeatability, or reproducibility of the results, and to explain how each change would improve your results.

So some suggestions that you might have made could include using a gas syringe to improve the accuracy of the measurement of gas produced.

Placing a temperature probe or a thermometer into the beaker to monitor temperature, that will allow you to monitor changes in temperature that would affect the rate of photosynthesis.

You might use a large beaker of water to act as a heat shield to stop the lamp from heating the water in the pondweed beaker.

Or use an LED lamp, which doesn't get hot, so that the temperature didn't impact the results that you received.

You could have carried out the experiment in a dark room, with only the light source illuminating the pondweed.

This will increase the accuracy as the pondweed will only be able to use light from the light source to photosynthesize.

Well done if you got any of those.

That brings us to the summary of today's lesson.

The results show that the rate of photosynthesis is faster at higher light intensity.

This is because light is needed for chemical reactions of photosynthesis to take place.

We considered the accuracy of the experiment through our analysis of the data produced.

Most results showed precision around the mean value.

Errors within the data can be identified.

These can be random errors, which are different each time, or systematic errors, when the measurements differ from the true value by the same amount each time we carry the experiment out.

And the results demonstrate repeatability.

The different methods of measuring the rate showed that the experiment also had reproducibility.

Thanks for learning alongside me today.

I hope that you enjoyed today's lesson, and I look forward to seeing you again sometime soon.

Bye-bye for now.