Lesson video

Hello, my name is Mrs. Holbrough, and welcome to computing.

I'm so pleased you've decided to join me for the lesson today.

In today's lesson, you'll be exploring digital inclusion using data.

What is a data set, and how can we answer questions using data?

Welcome to today's lesson from the unit, School Blog Digital Inclusion.

This lesson is called Investigating Digital Inclusion Using Data.

And by the end of today's lesson, you'll be able to interpret real-world data sets to investigate digital inclusion and access to digital tools.

Shall we make a start?

We will be exploring these keywords throughout today's lesson.

Let's take a look at them together now.

Data set.

Data set.

A collection of related data that has been structured in some way.

Correlation.

Correlation.

How variables move together, either in the same direction, positive, or in opposite directions, negative.

PPDAC or PPDAC.

A framework for us to follow when asking and answering real-world problems using data.

Look out for these keywords throughout today's lesson.

Okay.

Today's lesson is split into two sections.

We'll start by identifying patterns in data sets, and then we'll move on to apply the PPDAC cycle.

Let's make a start by identifying patterns in datasets.

A data set is a collection of related data that has been structured in some way.

So, for example, into a table.

A data set can be presented in a table, a chart, a graph, or a map, depending on what is most useful for understanding it.

So, for example, if we had a list of names and maybe their contact details like emails, we might put that into a table.

Charts can be used to visualize information, so to show us trends or the largest proportion of something.

And maps can be used to show us visual locations or maybe trends across different parts of the country.

This is an example of a data set.

The data is related to the weather, so you can see we have the days of the week.

We have a weather summary, and then we have temperature and rainfall.

It is structured into a table to make the data easier to interpret.

A pattern is something that repeats or changes in a predictable way.

We can find patterns in numbers, shapes, colors, tables of data, and graphs and charts.

In the weather dataset, we can see that most days had no rainfall or zero millimeters.

And you can see I've highlighted these in the table now.

Time to check your understanding.

What is the pattern of temperatures on sunny days?

Look at the dataset carefully and pause the video whilst you have a think.

Did you spot it?

Well done.

On the sunny days, the temperature was always 20 degrees Celsius or higher.

Identifying patterns in data sets can help us to ask and answer questions.

Sam has an example.

Is something increasing or decreasing?

That's a good example, Sam.

Sofia says, "Does the data repeat?

" Another good example.

And Alex says, "Is there a correlation between two variables?

" Ah, Sam has a question.

What is a correlation?

A correlation is how two variables move together, either in the same direction, a positive correlation, or opposite directions, a negative correlation.

This graph shows how much money has been saved over five years, so we can see the years plotted along the bottom of the graph, and then we can see the amount of savings mapped on the line on the graph.

What is the correlation between savings and year?

Maybe pause the video whilst you have a think.

Did you spot it?

Well done.

Each year, the amount saved doubles.

As the year increases, the savings also increase.

So, in this example, there is a positive correlation between the two variables.

A correlation means a relationship between two variables, but it doesn't always mean that one thing causes another.

Let's have a look at this data.

So, we have some months of the year.

We have ice cream sales, and we have sunburn cases.

The data set shows a positive correlation between ice cream sales and sunburn cases because both the ice cream sales and the sunburn cases go up together.

But, does ice cream cause sunburn?

Sam says no.

Both ice cream sales and sunburn are influenced by the weather, though, and that's a really good example.

So, although there is a correlation in this example, it doesn't mean that ice cream causes sunburn.

Okay.

We're moving on to Task A of today's lesson.

Identify patterns in data sets.

For part one, look at one of the datasets on digital inclusion, which have been provided as additional material for this lesson.

For part two, identify any patterns you can see in the dataset.

And then for part three, explain if there is any correlation in the dataset.

Pause the video whilst you have a go at the task.

Okay, how did you get on?

Did you manage to identify some patterns in the datasets?

Let's have a look at some sample answers together.

So, we've got Sofia here.

Sofia says, "I looked at the device dataset.

I could see a pattern that shows older children are more likely to use a mobile phone than a tablet to go online.

" And you can see here the dataset that Sofia was looking at.

So, we've got the ages and then the percentage who use any device, the percentage who use a tablet, the percentage who use a mobile phone, and the percentage who use a laptop.

Sofia says, "I then looked at the dataset which showed the percentage of children in the UK who have their own mobile phone.

And we can see that the 13 to 15 year age category, 97% have their own mobile phone.

" Sofia says, "There is a correlation between the two data sets.

I think more older children use a mobile device to go online because they are more likely to have their own.

" I think that's a reasonable correlation and explanation, Sofia.

Well done.

Okay, we're now moving on to the second part of today's lesson, where we're going to apply the PPDAC cycle.

The Problem, Plan, Data, Analysis, and Conclusion, or PPDAC cycle, is a framework that helps us ask and answer real-world problems using data.

Let's have a look at each stage of the cycle.

The problem.

This is where we define the problem that needs to be solved and pose questions that can be investigated.

Plan.

This is where we predict an answer to the question or questions.

Find a data set or make a plan to collect the data.

The data part.

This is where we gather the data.

You should then clean the data before moving on to the next step.

Cleaning the data means removing any errors or duplicates that might skew the results.

Analysis.

This is where we may visualize the data, spot any patterns, trends, correlations, or outliers.

You will write down your observations about what the data is showing you.

In the final section, the conclusions.

This is where we answer the question and explain what the data reveals.

You will decide on a conclusion, take action, or form further questions to investigate.

We have investigated data sets to see patterns or to extract meaning.

The PPDAC cycle is a framework that helps us ask and answer real-world problems using data.

Okay, time to check your understanding.

What does the P in the first step of the PPDAC stand for?

Is it A, data?

B, problem.

C, poll, or D, pattern.

Pause the video whilst you have a think.

Did you remember?

Well done.

The first P stands for problem.

Okay, what's the problem for our scenario?

Over the course of this project, you're going to be investigating digital inclusion and access to digital tools.

What problem are you trying to investigate?

And what questions do you need to answer?

Maybe pause the video whilst you have a think.

The next part is thinking about the plan.

What data or data sets are you going to need to help you solve the problem or answer your questions?

Where will you get this data from?

Again, maybe pause the video whilst you have a quick think.

In the data section, you're going to be collecting the data.

Is the data quality good enough?

Is the data accurate and reliable?

Where did you collect the data from?

Is it a reliable source?

Do you understand what the data is actually showing you?

Okay, time to check your understanding.

This time, I have a true or false question for you.

Data sets found online are always accurate and reliable.

Is this statement true or false?

Pause the video whilst you have a think.

Did you select false?

Well done.

While the internet provides access to lots of high-quality data from reliable sources, it also contains huge amounts of unverified, biased, or outdated informational data.

It's important to check the sources of information and verify the data you collect.

You can verify data by checking multiple sources.

Okay, the analysis section.

So this is where we're going to prepare the data.

You may create visualizations like charts, tables, or infographics.

These are going to help you present the data and write your conclusion.

The conclusion section.

How does the data solve the original problem or answer the original questions?

What are the conclusions?

Are there any follow-on questions that you may need to ask?

Data and statistics are powerful.

They help us to see patterns, compare trends, and make decisions.

But they can often miss.

Individual context.

Two people can be part of the same statistic, but have completely different lived experiences.

Emotional impact.

Numbers don't capture fear, joy, stress, resilience, or hope.

Inequality within groups.

An average can hide extreme differences.

Cultural factors.

The why behind the data often requires stories, background, and qualitative insight.

Outliers and edge cases.

The people most affected are sometimes statistically few, but socially significant.

It's important to consider these issues when you are collecting and analyzing data sets.

Sam says, "For example, saying 20% of students struggle with access to devices tells us the scale, but it doesn't tell us what the struggle looks like for a young person trying to complete homework on a phone late at night.

" Okay.

We're moving on to Task B of today's lesson, where you're going to apply the PPDAC cycle.

Use the template provided to apply the stages of the PPDAC cycle to investigate digital inclusion.

So you have a section for the problem.

What question are you trying to answer?

The plan.

What data do you need, and where will you get the data from?

The data itself.

Is the data reliable and accurate?

Here's the second part of the table.

So we have the analysis section.

What does the data show you?

Are there any correlations?

Is the data presented in a useful way?

And then finally, the conclusion.

Does the data solve the problem or answer the question?

What are the conclusions?

Pause the video here whilst you have a go at completing the table.

How did you get on?

Did you manage to use the template provided to apply the stages of the PPDAC cycle to investigate digital inclusion?

Well done.

Let's have a look at a sample answer together.

Remember, this is only a sample answer, so yours may look different, and that's absolutely fine.

So the problem.

What are the causes of digital exclusion?

Why do some groups of people have limited access to digital devices and tools?

So there's two main questions there that we're going to try and answer.

Plan.

I will need to collect data on how many children or families have access to the internet and digital devices.

It will also be useful to collect information about what barriers affect access to digital devices and tools.

Data.

The data collected comes from reputable sources, such as Ofcom, Nominet, and The Good Things Foundation.

I have checked the data sources and verified the information.

Let's have a look at the second part of the table.

The analysis section.

The data shows that 14% of young people between eight and 25 years lack access to a device suitable for learning.

7% of households struggle to afford broadband.

Difficulty affording communication services is most likely to affect households that receive means-tested benefits, 31%, or with a resident with a life-impacting or limiting condition, 39%.

And what about the conclusion section?

A significant proportion of young people lack access to devices suitable for learning.

The main reason for this is the fact that households struggle to afford devices and broadband.

Households on benefits or with residents suffering from a life-impacting or limiting condition are most likely to have difficulty in affording communication services, such as digital devices and broadband.

Remember, if you need to pause your video and add any extra detail to your table, then you can do that now.

Okay.

We've come to the end of today's lesson, Investigating Digital Inclusion Using Data.

And you've done a fantastic job, so well done.

Let's summarize what we have learned together in this lesson.

Datasets can help us identify patterns in digital access and inequality.

Correlation means a relationship between two variables, but it does not always mean that one thing causes another.

The PPDAC cycle is a framework for answering real-world problems using data.

Statistics do not always show the full story behind people's experiences.

I hope you've enjoyed today's lesson, and I hope you'll join me again soon.

Bye.