Everybody Makes The School is an exciting collaboration between The University of Edinburgh and Newbattle Community High School.

A school is made of not just bricks and mortar, but the people inside it. The students, teaching staff, and support staff at Newbattle Community High School all make a school community we can be proud of.

What do you want to tell people about your school community? Just like a school badge, a school motto, a school song – this is a way you can express what it means to be part of the school.

We used digital technology and data skills to show that everyone matters to the school, and that everyone makes an impact on it. We celebrate our school community through data.Learners gathered and processed data related to the school community – it’s beating heart. They analysed this data and considered playful ways to display it – through digital graphics, smart lighting, or sound. Learners created their own piece of public data-driven digital art to showcase what it means to be part of a Digital Centre of Excellence.

This project doesn’t focus on teaching programming or “digital making”, instead it’s the skills around data science (and the digital skills needed to capture, clean, and process that data) and the creative ways the results can be visualised.

The data produced in a school environment changes on a day-to-day basis, it’s reflective of the people in the school at that time, therefore digital art is dynamic. As the data changes so does the artistic representation.

Learners chose something they want to show or share about the school. They then chose a way to measure or collect the relevant data.  Finally they found a way to visualise it. The group worked in three teams to create data-driven digital art in three ways:
Rather than school motto: experimenting using graphical visualisation
Rather than a school song: experimenting using data with sound
Rather than a school badge: experimenting using data with smart lighting

The success of this project relies in its replicability. If other schools or places or learning want to run their own version they should not have to rely on it being delivered by computing teachers / technology specialists. The costs involved are low, not relying on expensive equipment or high speed Internet access.

Session 1: Introduction to Data Science

 

What’s covered in this session:

  • Introduction to Data Science
  • Hands-on Activity: Identifying Patterns in Data
  • What is “Everybody Makes The School”
  • Hands-on Activity: Data Science Challenge

Resources:
Get the slides with teacher notes.
Get the learners worksheet.

Introduction to Data Science

Start with an ice-breaker. Go around the class and ask learners their name and their favourite app, game, or website. Encourage them to give an answer that wasn’t given before.

Give Spotify as an example. A popular feature of Spotify is Discover Weekly. Each week it makes me a new playlist based on songs it thinks I’ll like.

But how does it know what you like? How does Spotify know if you enjoy a certain song? Do you play it a lot? Have you added it to playlist? Did you share it with friends?

What might I do if you didn’t enjoy a song? Skip it? Delete it?

Explain that Spotify uses a data analytics process to determine the type of music I might like:

Gathering the tracks I like
Exploring the patterns in those tracks
Predicting other tracks I might like

Hands-on Activity: Identifying Patterns in Data
Ask the learners to discuss in pairs (2 mins) the ways Spotify might determine what type of music I like. (Gather)

Once it gathers all the songs I like – it tries to find patterns (things that they all have in common). Encourage learners think about the types of songs they listen to. What sort of things do they have in a common: same style of music, same artist, same album?

Ask the learners to discuss in pairs (2 mins) the ways Spotify might determine what type of music I like. (Explore the patterns)

Once Spotify has found patterns in the data it then predicts other songs I might enjoy and adds them to my Discover Weekly playlist. Using patterns in the listening data, how might it choose to predict (or guess) other songs I might like?

Ask learners to discuss in pairs one way they might use a pattern to suggest another track. (For example, recommend a track from the same artist, recommend a track from the same genre). (Predict)

One of the reasons Spotify works so well at making predictions is they don’t just know what I’m listening to, they know what everyone is listening to. Millions of people use Spotify each month, so they can use all our data together to create better results.

So imagine, you’ve been paired with another user in Brazil. You’ve been paired because you both like a lot of the same music – even though you’ve never met this person Spotify knows you like the same music. Except maybe there is an album they’ve been listening to a lot recently that you’ve never heard. Spotify might infer that, since you like the same sort of music as that person, you’ll probably like this album too and recommends it to you.

But why are Spotify going to all this effort? Get learners to think about the benefits to giving good recommendations to their customers from a business point-of-view  (better service, more customers, willing to pay more money).

Ask learners what other companies do they know that do this too? How does it benefit those companies? What is the experience like for customers?

(Netflix, Amazon, Instagram)

What is “Everybody Makes The School”

So companies that hold data on their customers: it lets them learn about their customers, it let’s them make new products that suit their customers, it might help them solve problems. Just like companies hold data, schools hold data about us.

Learners should consider what data the school might hold on them already (names, ages, attendance, number of school lunches bought) and what data could be measured and gathered? (room temperatures, noise levels in corridors, movement in corridors, happiness of learners)

The slides give examples of the types of data that could be collected, and then interesting ways of presenting it:

For example, the attendance records of each year group could be converted into a symphonic string tune –  a new soundtrack for the school. Notes created by data from S1 and S2 have a higher pitch than those in the upper school. The longer the “streak” of perfect attendance, the longer the note. This composition uses real attendance data. Learners must consider how this data must be stored and cleaned to prevent identification.

Or, a “mood cube” that reflects the current activity level of the school.  The light is brighter the more people are moving around the school. The colour of the light changes depending on what sort of activity is taking place (class time, break time, assembly time). This could be made using Philips Hue bulbs and Python code.

Hands-on Activity: Data Science Challenge
How many smarties are in a tube?
How many of each colour of smarties are in a tube?

Learners can use data analytics (Gather -> Explore -> Predict) to make their own predictions to answer these questions.

Learners should work through the steps on Worksheet 1. To create the visualisations learners could use a spreadsheet.

You’ll need to have lots of little boxes of smarties (or other sweets) – the funsize ones work well. At the end of the lesson use an unopened packet of smarties to see how accurate the predictions have been. How might they improve their predictions? (More data?)

Session 2 Part I: What is an Internet of Things?

 

What’s covered in this session:

  • What is Internet of Things technology and how is it used?
  • Hands-on Activity: Build your own IoT using BBC micro:bits

Resources:
Get the slides with teacher notes as a PDF
Get the slides as a PPT

Micro:bit

 

What is an Internet of Things?

Start by a recap of Session 1: An Introduction to Data Science. Did anyone impress their friends with predictions about smarties? Did they start to notice any data science practices in their own lives? Remind them of the project brief.

Introduce the next session about the Internet of Things.

Work on IoT is being done at Edinburgh University by Dr Jeremy Knox and Dr Michael Gallagher Centre for Research in Digital Education at the University of Edinburgh http://www.de.ed.ac.uk/ Many of our slides come from this work.

Explain how it used to be about computers connected to the internet communicating with each other or us communicating through the internet by email. A “thing” in an Internet of Things can be anything or anybody connected to the internet. What things can be connected to the internet nowadays?

Ask for examples of “smart” devices.

Discuss the different types of connections over an IoT e.g. human to human, human to machine or machine to machine. Discuss how these things are connected and “talking” to each other.

People to People: Give example like WhatsApp and Facetime – technology that connects people to people.

People to machines / machines to people: A person uses technology to search for a TV show on iPlayer. Shopping websites like Amazon sends an email to a person when their order has been dispatched.

Machines to machines: An alarm clock goes off and this signals your kettle to turn on. When your milk runs out more is ordered for delivery. Your home security system detects an intruder, phones the police and sends you a message.

So what exactly is IoT?

Using sensors to collect data and using that data to drive some kind of technology, and to develop some kind of activity.

Explain that each “thing” has to have some way to collect data, using a sensor as the input. This could the sensor on the buttons of a keyboard or a light or motion sensor:

Ask what sensors are used to detect heat or sound? What sensors does your TV use (infrared) or carbon monoxide detector (chemical/electrochemical)?

Ask what other types of sensors there are:

  • Temperature
  • Proximity
  • Pressure
  • Water quality
  • Gas
  • Smoke

Explain that the “thing” takes the data that it has collected using the sensor,
and it can then do something with the data itself, or share it with another “thing” on the network.

If someone rings the doorbell then place a video call to your phone.

If it’s getting cold outside then turn on the heating

Can the learners think of other useful technology using if and then commands?

Show the learners the BBC micro:bit and explain how it is a good example of a “thing” they can easily use to practice building an IoT:

  • A micro:bit is a pocket sized device you can control with code.
  • It’s a micro controller – you write code on another computer and then upload it onto the micro:bit. It can only run one program at a time, but you can reprogram it as many times as you like.
  • Ideal for an IoT project as it’s small
  • Has various sensors; temperature, light, bluetooth, accelerometer

Show the front of the micro:bit and point out the LED lights and buttons.

Show the back of the micro:bit and pint out the processor, ARM and sensors.

Hands-on Activity: Build your own IoT using a Microbit

Explain that the learners will now create their own IoT network using Micro:bits by using the bluetooth function to send text or image messages to each other.

  • Direct the learners to https://makecode.microbit.org
  • Get micro:bit to display your name when you press A
  • Decide a group number with another pair and use the “radio set group” block to make sure you are on the same radio group
  • Use the radio blocks to send your name when you press the A button
  • Use the radio blocks so when you receive a string with someones name you display it on the screen

Make sure every group is using a different number.

Finish off by getting everyone onto the same number we decide as a class.

This should get them thinking about how a “thing” like a micro:bit can be used to collect data then pass it on to other “things”.

 

Session 2 Part II: Exploring Digital Art that makes use of IoT Technology

What’s covered in this session:

  • How people are using IoT technology to create digital art

Resources:
Get the slides with teacher notes as PDF
Get the slides as a PPT

Exploring Digital Art that makes use of IoT Technology

Start by a recap of Session 2 Part II: What is an Internet of things where we’ve looked at ways IoT devices can collect data and talk to other devices.

Now we are going to look at how this can be used to create digital art.

Light Reminders

Visit webpage http://light.friendrnd.com/#/ and explain that Brian Foo set up different lights in his home to represent a friend where the amount of light emitted by that lamp represented how often and recently they interacted.

Air Play: Smog Music

Visit https://vimeo.com/122603843 and explain that Brian Foo used 3 years of daily air pollutant measurements to alter the sounds and visuals over the duration of the song.

Living Light

Living Light is a permanent pavilion that displays a giant map of Seoul that glows according to air quality and public interest in the environment.

  • Brighter areas of the map show where air quality has improved and dimmer lights show where they have worsened.
  • Every hour the pavilion goes dark before lighting up at each neighbourhood in order of best air quality.
  • Citizens can text the pavilion with their post code to receive a real-time report on air quality and this text causes their neighbourhood to blink to show the citizens’ concern.

Look at some other examples like eCloud and Listen to Wikipedia or find some examples of your own.

Ask the group to think about how they could use technology, for example the micro:bit to create their own piece of music or visual display and the different stimulus that could drive this for example changes in temperature or pushing different buttons?

Discuss how the creation of digital art by IoT could be used for a school project? Is there any data the school holds or could collect from the community that could be used to create data-driven digital art?

Ask the group for examples of things that could be counted and measured. If not already mentioned suggest: classwork, people, homework, assessments, awards, results, temperature, attendance, noise, hobbies, recycling and habits.

Discuss what interesting outputs or visualisations could be used. If not already mentioned suggest: moving sculpture, playing a musical note, lighting up the floor, water wheel turning, bubble machine, fanfare, water balloon launched, confetti.

Discuss. Connect. Design

What is it celebrating or showing off? What is the data? What is the output?

 

Getting Feedback from Learners

 

When it comes to collecting data about learners it is important to be open and transparent. After all, the workshops explore the power and value of data and how it might be used.

I added a disclaimer at the start of the feedback form. This let them know all questions were optional, and all responses were anonymous. I also made it clear the data collected would be shared with the project partners: University of Edinburgh and Newbattle High School.  Your feedback form should be adjusted to suit your needs and uses.

I chose to use a paper feedback form rather than a digital version. I felt this was quicker to fill in rather than asking students to navigate to a website. The downside was I had to type the results into a spreadsheet manually, but that didn’t take long as it was only a small number of forms.

On the feedback form I only asked 4 questions:

  • Circle the emojis that best describe how you felt about the session
  • What did you learn today that you didn’t know before?
  • What did you enjoy most about the session?
  • What did you not enjoy? What would you change?

The responses gave me enough evidence to gauge if the workshop had achieved its objectives. Did they enjoy it? Did they learn something new? The responses also offered an insight into ways it could be improved.

You can download the feedback template here as a Word document, or as a PDF.

Author: Craig Steele, Digital Skills Education –https://craigsteele.com/

Posted on:

21.01.20