Philip Mallon: STEM Clubs and AI-powered projects 

00:00
Philip Mallon, thank you for joining me in another podcast, another Tech Explorations podcast. It's been a while since the last time we did one of those. So how have you been?

00:11
Good, thanks. In good health. And also, I've been quite busy. So, you know I'm retired. I've actually did quite a lot of work.

00:22
I don't think there's such thing for you as retirement, like you're probably busier now than ever before.

00:28
What I can say here is that you've been very busy. So we'll talk about this in this podcast, I think. That's why you're on.

00:35
Maybe to begin with, because again, I remind our viewers and our listeners that this is not the first time that you've been on the Tech Explorations podcast. You've actually done a few more episodes and I'll link them down below in the notes for people that want to get some background on Philip’s

00:50
extensive history with technology is going back a long time. He's done some amazing things in engineering and in computers and in education lately.

01:00
But in this specific episode, I'd like to focus on your work in the context of education. Maybe in the last year or so last few months.

01:13
Maybe give us a quick summary of what have you been up to in the last year or so?

01:20
In the last year, I've joined the CSIRO as an industry rep in their industry STEM program for schools. So I've been doing that. But I've also been

01:37
Philip, sorry to jump in. What is the CSIRO for those of us that don't know about it?

01:42
Oh, the Commonwealth Scientific and Industrial Organization.

01:48
So in Australia it's like the premier research, like a civilian research organization.

01:54
Yeah, it's a premium. It's funded and it's really part of the government, the federal government. And it does fundamental research, but also there's been a lot of spin offs like Wi-Fi came for the CSIRO.

02:13
Life has never been the same again. Yeah, that's right. So even the first use of a computer for making music came for the CSIRO.

02:24
So the synthesizer? Yeah, so things like using computer technology in music is fairly common these days, but it goes back to radio astronomy.

02:37
And the fact that they were able to identify this as a potential application and demonstrate it. I think it's quite interesting.

02:47
Absolutely. Yeah. So now, like this is an amazing organization for anyone that wants to look it up. A lot of good stuff comes out of it. Now you're part of it. Tell us about it.

02:57
Well, with the CSIRO, they're very active in environmental research and in recycling.

03:06
So what I do in my STEM programs, they're really managing STEM clubs. And it's not only with the CSIRO I'm working with. I'm also working with local councils.

03:17
And that includes Blacktown Council and also one Parramatta Council. And for each of the organizations, I'm running STEM clubs.

03:28
So they add on to an existing school program normally. They don't follow a strict curriculum, but they are project based.

03:39
And I use bits of my career, experiences from the CSIRO and themes that are happening in society today as a contextual base for the projects that we're working on.

03:56
So the projects you're working on are connected to what's happening in society today. They are, I guess, topical projects. Can you tell us about how those STEM clubs relate to the schools.

04:12
I think you mentioned that they are connected to a school. Is it like a class from a school where you visit that school and deliver a project program?

04:25
Well, for the two schools that I'm working with, with the CSIRO, one is that Chatswood High School and the other one's Ravenswood Girls School.

04:39
Now, both of those schools, the kids are already doing a formal STEM education, but they want to take it a bit further.

04:52
And the other thing about this STEM education, it only tends to go to about year 10.

05:03
So years, upper primary six, then when we go to secondary year seven and eight, it's reasonably active, but then it steers off and goes back to the old methods of working in science and individual subjects.

05:20
But the kids, they want to take up STEM a bit further than what they get in school.

05:29
And they can see that perhaps this could get me some more skills, and it might even lead to a career later on.

05:40
Yeah, I've been thinking about that. I guess at some point in school, kids have to focus on things like exams for entry into university. So they have to focus on curricula.

05:51
And what's going to be in the exams, right? And those extra extra STEM curriculum activities tend to take second place.

05:59
That's right. So I've noticed, for example, when I have workshops in the school holidays, I get higher attendance.

06:06
And I also get attendance from the kids who are a bit older, and from the selective schools too.

06:13
But during the school term, I don't get any of those. I get the younger kids.

06:19
Of course.

06:20
The older kids are too busy with their homework or with sport or with other activities.

06:25
So it's quite interesting that there's a different approach, depending on the time that they have available.

06:33
Yeah, it's just the stresses of modern education. I guess the objectives being for many of those kids entry into university and how that system is structured to help them get into particular university school.

06:47
We can talk about that. I wanted to also take a step back and just so I was thinking about how did we reconnect because we haven't spoken for a few years.

06:57
And what happened was that a few weeks ago posted something on my blog about ChatGPT and artificial intelligence and the latest developments over the last few months.

07:10
And then you jumped on to the conversation. And that's how we got reconnected, because you, you haven't been using ChatGPT specifically, but you've been also integrating and including artificial intelligence into your into your STEM projects.

07:27
And I remember in a previous conversation, you mentioned something about visual object recognition, for example, which I think is part of your STEM recycling projects.

07:38
So without getting into specifics yet, we could get into the specifics a bit later, could you tell us a little bit about the relation between AI and your STEM projects.

07:47
Yeah, I've been working on AI for about three or four years now.

07:52
But more recently, I've added the newer tools like ChatGPT and also Google Bard.

08:03
But the previous work that I was doing was using the, the tools from Google are called Teachable Machines.

08:12
And there's also Microsoft have a tool called Lobe.

08:22
And those tools are used in the Chatswood High School, because they said they couldn't manage rubbish.

08:31
And I thought artificial intelligence might be one of the potential tools that could be used, besides other tools.

08:40
But the artificial intelligence that I'm looking at, goes back to not only tools, which are central, like Google's and Microsoft, but also tools that are on the edge.

08:55
So they're called edge AI. Yep. And little tiny computers, like the Raspberry Pi Pico, I'm using those to teach them how to make inferences about some of the signals that they get.

09:16
So, I've been doing a little bit of work on that. And that's been quite interesting. So I use a tool called Edge Impulse.

09:23
And a lot of it goes on to either the Raspberry Pi or the ESP 32.

09:30
Yeah.

09:31
Well, what kind of signals are those like we're talking about visual, audible or, let's say, sensor signals like temperature, humidity, things of that sort.

09:41
Well, as an example, I can get the game going, cities, paper and rock going, either with a camera, or even simpler, just using a photo electric cell.

09:56
And with a photo electric cell, it's able to look at the patterns from the fingers and actually make some interpretation of what the, what the gesture might be for the game.

10:12
So you can actually simplify the centers, but you look at patterns, time sequences or patterns to make some inference.

10:23
So I guess you're using edge AI, which is able to make those inferences on the device itself without having to communicate with a large AI model like ChatGPT which lives somewhere on the cloud.

10:42
So you can train the inference model or the inference engine on the device itself. Right?

10:50
So it stays local. And I guess it's fast. Is that what the objective is there.

10:56
Yeah, the objective is to make it as reliable as possible. So in certain circumstances, you might not have the internet available, or maybe down.

11:07
That doesn't mean to say that your application has to totally shut down.

11:11
You can still operate, particularly in critical situations where there might be safety involved.

11:19
And so it's really a mixture. It's been able to, to get the best out of the internet and cloud services. But at the same time, be able to make it reliable.

11:34
Right. So it's a hybrid model. What is the underlying technology behind edge AI, is it like neural networks, expert systems like and do you teach those fundamentals to the students.

11:50
With Chatswood High School, we actually did. We went through that. And the teachers there were also teaching years 11 and 12, and wanted to use some of the concepts that we were exploring for the older students.

12:09
Well, I thought that was interesting. But yeah, we're using neural networks.

12:15
And we're teaching the machines patterns. So there's a pattern there that we go through in identifying the signals that we're interested in and associating those in a teaching model.

12:31
It's actually quite simple. It doesn't involve higher level databases. We have very, very simple. For the edge AI is actually very simple.

12:42
And for thing where I start using a camera, then I need a much larger data set with bigger and quite often for rubbish.

12:52
I scour around the internet and I use other data sets and build on top of what other people have also done.

13:02
It's, it's interesting.

13:04
As I see it based on your description, you are basically teaching students, humans, how to teach a machine to learn.

13:15
Through, in this case you mentioned rubbish by looking on the internet for photography images of rubbish that you can use as training material for the edge AI.

13:27
So you're teaching humans how to teach machines so that the machines can then make decisions that can help humans out in this case with recycling of rubbish.

13:38
Is that right? Well, the problem was that a school wasn't managing its rubbish program very well.

13:45
And the concept that I had here was that if the students were getting feedback, it could improve their behavior.

13:54
But if they were putting plastic in a compost bin, for example, the computer could identify and tell students that's incorrect.

14:05
Yeah, so are you actually building some kind of rubbish bin that can see what kind of rubbish is being put in it and provide feedback to the student or to whoever puts a rubbish in as to incorrect bin, take it out.

14:24
The models that we went through are still desktop models.

14:28
They were intended to go out into the playground later on.

14:34
But that would have to go through a couple of extra steps.

14:38
Of course.

14:39
And one of those would be to improve the communications.

14:42
So we might even use security cameras instead of the PC cameras, which are in the classroom.

14:49
And I do that at home. I've got my security cameras all wired up into artificial intelligence.

14:55
So it means that I've got the cameras wherever I want them.

14:59
And also for communications, the cameras are very close.

15:04
So there's no problem there with the communications.

15:07
But outside, if you've got a remote camera, you might have to look at other forms of communicating.

15:15
And one of those was we're exploring the use of LoRaWAN.

15:19
For a big school that had quite a distributed area.

15:25
So that was one concept.

15:27
For long range communications. But if I understand right, you're talking about communicating

15:32
the inference that the edge AI has made about, for example, a particular type of garbage been put in a bin.

15:40
Communicate that information of correct or incorrect, I guess allocation of garbage into bin to the student, right?

15:48
That's right. The idea.

15:50
Yeah.

15:51
The idea would be that anywhere in the school, which has got quite a wide area for playgrounds and sports fields that we could put a camera anywhere.

16:02
And the camera could make decisions. And only the decisions, not the image would be fed back to a logging center.

16:15
Ah right. So then you could collect statistics on, for example, how many correct or incorrect deposits of garbage in bins have been made.

16:23
And then that information can be used by students and staff to infer on the, I guess, correct use of recycling bins, right?

16:36
That's right. It would give us an overall picture.

16:40
Considering the fact that we had quite a wide area to cover.

16:44
It's interesting application.

16:46
Yeah, sorry. I'm just thinking...

16:48
Our project was only still desktop based.

16:51
And at the desktop, it relied on the feedback from either the Microsoft or the Google model.

16:59
Now, the Google one, I actually took it a little bit further so that students could be interested in their environment.

17:07
And the way I did that was to have a database of bird calls.

17:14
And so the students were able to identify the species of birds by the sounds that the birds were making.

17:22
Yeah.

17:23
The Google one not only allows you to use images, but it also allows you to use sounds.

17:31
And we've got it. I've got a complete model now.

17:34
Someone else has done this on a Raspberry Pi.

17:37
And we've now got a program called birdnet working that can identify all the birds in our local area.

17:47
So, you got a microphone connected to a Raspberry Pi, which listen out for birds and then just, I guess, on the screen will tell you what bird it is.

17:56
Well, we've got about how many cameras in our house, perhaps about five or six cameras, security cameras.

18:02
And the audio of all of those goes into the birdnet data logger.

18:09
So we've got a whole property surrounded.

18:11
And it's quite interesting because we've made about 10,000 sightings over two months. And identified.

18:20
I thought there was only about one or two species of birds.

18:23
But there was 26 species of birds identified using this model. In your neighborhood.

18:29
Yeah, in a neighborhood. Now, the purpose behind this was to connect students with nature and with the environment.

18:41
So that there'd be a closer appreciation and they'd be able to see what's happening and know a little bit more what's happening outside.

18:51
Yeah, I thought that was quite an interesting extension. It's a separate project, but it's also part of the model that I'm interested in.

19:01
That's very interesting. So, I guess, there you are covering quite a wide spectrum of what applications AI can have in daily life.

19:11
You've got video, you've got the audio, you could obviously put in other sources of signals, various sensors.

19:22
It could be atmospheric sensors, humidity, temperature, various gases.

19:28
So you could use all that to, I guess, automate the world around us and extract information that can be used to make decisions.

19:38
In this case on environmental impacts of our activities.

19:42
Yeah, it's not only environmental. I've also applied it in our STEM club for music.

19:50
Yeah.

19:51
And that was quite interesting because the kids there, I try to make connections of what they're interested in.

19:59
And some of them are interested in football. So we said, Oh, well, how about we use AI to come up with the theme song for your local football club.

20:11
And what also come up with a new logo for your football club.

20:18
Only using the colors of your football club without telling though what those colors are.

20:24
And what's interesting, it was able to, the machine that we used was ChatGPT, and also I used Bard too.

20:34
But some of the kids had never really experienced what AI is like.

20:39
They might have heard about their parents talking about it.

20:42
Yeah, we actually made a connection of what they could be interested in.

20:47
And they were really interested in what was happening.

20:51
So you link...

20:52
Yeah, you link it to something that is like very interesting to them, like football and the football team.

20:59
And then you apply Artificial Intelligence, they are immediately interested in the outcome.

21:05
Because of these two topics now play together. It's not just an abstract application of AI and some topic that they're not interested in.

21:13
So it's very clever. You need to know who you're talking to, right? In education it’s like as important as in every other part of life.

21:21
You need to know your audience.

21:23
Exactly. The other thing was that I'm covering quite a wide range.

21:28
Most of the coding that we do uses Blockly or MakeCode, which is quite good for younger kids.

21:37
But then I talk about Python, and they can see how the Blockly is translated.

21:43
But then I say, well, if you tell ChatGPT, what you want.

21:49
And particularly if you tell it that it's for a micro:bit computer.

21:54
It can come up with the code. And we did that with music. We said, create a program for a micro:bit computer that can play music.

22:05
And again, they were quite interested in that because they saw two different ways of doing it. One, they did it on MakeCode.

22:13
And this other way, all they did was to concentrate on the requirements or the objective.

22:21
And they could express that into ChatGPT. And it came up with some interesting answers.

22:28
It's a good start, isn't it? Like you can use prompts in ChatGPT to get you started with something that you want to build.

22:37
Like create the first draft of the first prototype. And then you can improve from there.

22:43
But I guess if you look at it as an assistant in education as well, as well as in other parts of creative life that that can be a way for students to get started learning a topic using ChatGPT as an assistant.

22:58
And having the role of an assistant is quite important there because you don't want it to do the work for you.

23:09
You really have to have a conversation and correct what you think might be a problem with the code that it gives you back.

23:21
And I think what's interesting, I took this to another level just yesterday, but my son.

23:28
He's quite an expert. He works at the CSIRO.

23:33
And we collaborate a little bit. And we did this yesterday.

23:37
And he's told ChatGPT that the code for controlling cameras on our security system was inadequate.

23:49
And after about nine iterations. He eventually got ChatGPT to come up with code, which was workable.

23:57
Yeah, yeah.

23:59
I've got similar experience with that as well. I've used ChatGPT to help me write Ruby code, quite a lot of it in the last couple of months.

24:09
And yeah, it often starts with a prototype that doesn't quite do what I wanted, sometimes got bugs, but through a few iterations just like to do with your own code.

24:20
It eventually gets there. And the really interesting thing for me from a programming perspective is that iterating with ChatGPT's help is much faster than iterating

24:34
using the usual googling around method. It's just, it's just ChatGPT is much faster.

24:41
It won’t write the code for you, but it will help you get there much faster than without it.

24:46
Yeah. And so as an example, with students, you really have to go through that iteration to show them that the first results that you get might not be satisfactory.

24:58
That they shouldn't submit that in their own assignments, for example. But the role might be that they could use ChatGPT as someone to check their final code and then make a statement about the feedback that they get from that.

25:18
Exactly. You can also ask ChatGPT to analyze code, explain how it works, explain particular lines of code, or explain the whole thing, or even you can get it to rewrite a piece of code that you've already written, but make it better make it more efficient.

25:40
You can, we can parameterize it and say, make it better in this way, for example, make it faster, improve the variable names, you can even do that.

25:49
So I find that also amazing capability also from an education point of view. Another thing that I've done is code that I do not understand, because, for example, I got it from someone's GitHub repository.

26:05
I put it in ChatGPT and asked it to explain it to me, explain what it does. For a student that is also amazing.

26:16
Yeah, it's got a long way to go.

26:18
Still, yeah.

26:20
In the STEM club environment.

26:22
And also, there's been a recent interest in getting it to actually compose things like music.

26:29
Yeah.

26:30
It's sort of moving into your territory.

26:33
It's not the only tool that we use we use other tools like simulation.

26:39
High level simulation, and some of these tools are more appropriate when you're talking about bigger community projects or problems, like say bushfires or COVID-19.

26:51
We can actually use mathematics and simulation models to explore the dynamics of some of these things.

27:02
Did you want to talk about that now, Philip, or because I'm thinking we could actually work on the mind map that you prepared earlier and work our way through it.

27:16
So, just to provide our views in particular, a visual assist in the things that we're talking about, which I'd like to switch to your mind map and then work away around that.

27:28
Yeah, sure.

27:31
Yeah, I can put it on the screen right now. There you go.

27:35
Okay, so there's a mind map. So this is basically your work in the STEM recycling project.

27:42
Right?

27:43
That’s right. So the artificial intelligence is only one tool. So we let the students see that you can tackle the problem from different perspectives.

27:56
And the resources they've got available are things like arcade games, like Scratch, right through to using the Microsoft Arcade called MakeCode Arcade games.

28:16
Also, things like centers, cameras and other centers that can be used to make a small project. So if you look at some of the projects, the students over about three weeks were able to put together on a micro:bit base computer

28:38
that had an arcade console attached to it. So it looked a little bit like the old game boy.

28:48
And they would come up with a game that related to rubbish collection. And a good example that was that one of the students was able to make the game for picking up plastic in the sea.

29:07
So yeah, that was a bit of fun. And when you think of it, a game boy was something that might have been popular for students 20 years ago, but not many of them actually used it today.

29:22
But you can do the similar things with Scratch and come up with like a scenario, which would allow students to interact with things like plastic and the playground.

29:37
And the whole idea is if they're talking about it, they'll become more conscious about the problem and hopefully it'll change their behavior outside.

29:47
Philip we don't have to touch all of that. There's a lot of details here and obviously we are always short of time. But I guess this is interesting. Would you mind taking a few more minutes to get into the specifics of the Pick up the Plastic game.

30:00
Like how did you build it? What's the objective? How does it work? Maybe to get an idea of this particular as a specific example of your STEM club projects.

30:13
That particular example, was done by one of the students themselves. So I would go through and introduce the concept of arcade games using MakeCode.

30:27
And then I'd give them a couple of suggestions. And the one of picking up the plastic in the sea was one of the students from Chatswood High School completed that game and was able to demonstrate it.

30:41
Over Zoom. So they did it themselves. And they came up with a working model. Now the idea was they would have a fishing boat, which would go out and would be able to distinguish between a sea life and plastic bottles.

30:59
And what they had to do was pick when the fishing boat got close to a plastic bottle, it would pick it up and it would get some points for doing that.

31:10
And if it did anything that would degrade the environment like catching too many fish, it would lose points.

31:20
So what about the specifics on how would the boat know what kind of object it's about to pick up.

31:29
Would that be like the AI application of video and edge AI.

31:35
There's no AI involved in this one. It's actually quite a simple one of how close the boat is to the objects, and it then assumes that it can pick it up.

31:47
So it's like floating on the surface.

31:51
Then depending on proximity, it may be within the range of, I guess, it could be an arm or some other kind of, I guess appendage

32:00
that could pick up the object, put it in, classify it as fish or plastic and get points?

32:06
What they would have to do is use the direction controls, and you can see this on the screen that the boat could be moved around.

32:16
Oh, right. Like an arcade game. That will be an arcade game then on the screen. Yeah, got it.

32:21
Hopefully an arcade game. Another one would be that rubbish would come falling down.

32:26
And you could use an existing model like Space Invaders.

32:31
So when the rubbish comes close, you have to get the right bin to pick up whatever rubbish is falling at the moment.

32:41
So quite often you might actually get a model of an existing scenario and modify that in the context of the environment.

32:53
Yeah.

32:55
Nice.

32:57
Could you tell us, this also looks interesting to me, the Rubbish Classifier.

33:03
What's happening here?

33:04
Well, the rubbish classifier was where the cameras were used. So the camera on the PC would be able to look at, say, some rubbish, like a plastic bottle.

33:18
And it would be able to identify as a plastic bottle, instead of, say, a metal can or a piece of paper.

33:28
So it's using the AI approach there and coming up with a classification for that rubbish.

33:36
We spent more time on that, but it would only become a component in a complete recycling project.

33:49
Once you're classified it, you can then put it in either in the right bin or you can then give feedback.

33:57
So the idea would be later on that you can also identify the color of the lid of the bin.

34:05
So you can then associate the bin with the rubbish that you have identified and then give feedback to the student.

34:12
Like which bin they should put it in.

34:14
That's right. Or alternatively, you can identify the rubbish and then you can have a LED that would light up the correct bin to show which bin you would put it in.

34:28
But it wasn't a total automatic project where you would just let the bin do everything. It was also to improve the involvement of students in the cycle.

34:45
But part of that was...

34:45
Awareness.

34:47
That’s right. Awareness and improving it because we might only have a couple of demonstrations like that, but there would be far more bins in the school grounds than what we would have tools for.

35:02
So part of the problem there was also to try and see what happens.

35:07
Putting the rubbish in the bin is at the end point of the rubbish and this is where the simulation came into it.

35:14
It was able to look at the whole cycle and with the CSIRO, they refer this as recycling economy.

35:27
So what happens with the rubbish? It might go somewhere else.

35:31
And if it goes to the wrong place, it could end up in the sea and the plastic could cause problems.

35:38
You wouldn't be aware of that.

35:40
So you depend on the proper management of how that rubbish is processed later on.

35:48
And this is where we use NetLogo. It was able to come up with a community that looked at the total cycle of recycling.

35:59
So NetLogo is a simulation application app. You use NetLogo is to simulate the life cycle of an object that becomes garbage and then gets recycled?

36:10
It's a simulation environment that allows you to create code and also visualize the outcomes of that.

36:20
So you can apply the problems which are more complex than what you would normally do with Python.

36:27
So if you want to look at how a bushfire might spread or how the economy works, it works in areas where there's

36:38
quite a lot of interacting agents or interacting objects.

36:45
And so it can be used in social, economics, but also used in physics and chemistry.

36:52
So you could use it for things like looking at how Newton's laws of motions work.

36:58
But it also works in things like well, in chemistry, where you might have quite a number of interactions working,

37:05
you might use it in that area.

37:09
Adding biology is a really good example. We have got a prey and a predator.

37:14
And you want to see how a population could grow or die out.

37:20
If there isn't the right balance between the two of them in a group.

37:29
Yeah. Yeah.

37:31
We use that for recycling, but we also used it for traffic lights.

37:36
So we built traffic lights with LEDs and so they can actually look at a single traffic light.

37:45
Or they could build it and come up with a matrix of something like 20 by 20 intersections and see how that worked.

37:56
And what would happen to the traffic, which is really hard to do just using electronics on a model.

38:05
But using NetLogo we’re able to build this wider model and then integrate a single traffic light into the bigger picture.

38:16
So then you would try to, I guess, optimize traffic and get the sequence of lights going green, red, etc.

38:25
To ensure that as many cars can pass through the intersection in a given unit of time.

38:34
That's right. There could be some points of injecting lots of traffic and side roads that have less traffic.

38:41
So you have to come up with the right, the optimal timing for the traffic lights.

38:47
But you could also look at, well, what would happen if one of those lights was not working.

38:52
And with the NetLogo program, you could actually see things like congestion and gridlock.

39:01
So simulate how effective the light system would be in different conditions, different times where you've got more or less traffic, peak hour, off-peak, etc.

39:14
That's right.

39:15
Test the model in the simulation.

39:17
And the students, they were using existing models, but you could code the whole thing yourself.

39:25
So there is a script language, which would allow you to create variables.

39:31
It was object orientated, so you could create other parts in code yourself or alternatively what we did.

39:41
There's a lot of published programs already, and there are books written on this.

39:46
So you could use existing material, play with the models, learn a little bit about the relationships and interaction.

39:54
But you could then go to the script and make adjustments.

39:58
Yep.

39:59
So the model on its own, would allow you to make changes.

40:07
So you could make changes to the model and see what the outcome was.

40:11
Yeah. Iterate as usual.

40:12
But you could also go back to the code and change that to if you wanted to.

40:16
Yeah.

40:17
Open source.

40:18
That’s the benefit.

40:20
So Philip, because we are about to run out of time, I wanted to focus on students now. So we talked about the projects and I can see that you're very excited about them.

40:29
Can you tell us what is the effect that these projects, all this work has on the students themselves and just

40:38
being mindful that some of the students actually in a very stressful part of their student life, getting ready for exams and year 10 and 11.

40:49
Where this is not strictly speaking curriculum and the kind of thing that will get them into a university degree.

40:57
So how have you seen students changing as they're going through the STEM club projects.

41:04
Well, as a STEM club, it's not as rigorous as what they would do in a formal program at school.

41:11
But at the same time, you do need to get some feedback on whether the students like what they're doing.

41:18
And so what we do, we reserve about the last five to 10 minutes of any STEM club session.

41:26
A STEM club session could last, well, in Ravenswood they have half an hour.

41:31
At Parramatta they have one and a half hours. But I always make sure that right at the end, we do a reflection and we get feedback from the students.

41:43
And also the library themselves has a feedback program.

41:49
And generally, it's been good feedback where the students enjoy what they're doing.

41:56
And they come back again.

42:00
So that means that they are learning in a like a low stress environment.

42:06
Which I guess, I'm not a psychologist, but I'm sure that's a good thing versus learning in a high stress environment.

42:13
Right?

42:14
The idea is that we use things that they're already familiar with.

42:19
So I use Lego to a large extent, and we can build also Lego models.

42:27
So I'm looking at things like how they could make a robot themselves with Lego.

42:33
And most of them have played with some of those tools before, so we've already broken the ice.

42:40
Because we're using things they're familiar with, then we use analogies that relate to Lego and modular programming using MakeCode.

42:53
So we make associations there based on what they might know already.

43:00
Yeah.

43:02
Do you think that, I guess participation in a STEM club is voluntary obviously no one is forcing any kids to join.

43:12
But I wonder, number one, I expect actually that a lot of the kids that do participate are already inclined to technology and they are naturally curious about it.

43:23
Have you come across kids that decided to join, even though they're not keenly interested in technology.

43:31
They might be interested in other things.

43:33
But because they saw it as a fun activity, perhaps from their friends, they thought I'll try it out? Have you experienced any such kids?

43:43
And if yes, did that kind of experience in the STEM club

43:49
change what they wanted to do in university, for example, instead of going into a literature degree, deciding to do engineering.

44:01
The time that they spend in the STEM club is only one term, generally, and with Parramatta, for example, there's a waiting list.

44:11
So it's quite popular.

44:16
But the problem is that I'm not there long enough to see what the longer term impact is going to be.

44:24
I guess the type of the ways that I'm looking at it, the feedback in terms of they're coming back to the club.

44:34
They don’t have to. But they come back all the time. So I'm not losing many students.

44:41
So they are interested in that.

44:44
They're also interested in the point of view that they're interacting by asking questions and making things.

44:50
But I have to be careful not to make it too hard.

44:53
If they've only got say 30 minutes, it becomes a real challenge for them to do something.

45:00
Yeah.

45:03
If they have enough time, I have to make sure that the project that I'm presenting is doable.

45:11
Yeah.

45:12
They would become frustrated if it didn't work.

45:16
Exactly. So you need more time.

45:18
There's some things that you can't rush.

45:23
Yeah, you need to... And time is a big component of obviously rushing things through.

45:30
Which degrades performance and makes it harder for everyone.

45:34
As an example of that, I'm just going through timing how long it takes to make a robot with Lego.

45:41
And I've got it down to about three minutes now.

45:44
You can make a robot using the right parts in a short period of time.

45:50
Or you can take up a whole of the period just mechanically constructing them and you don't get on to the software.

45:59
Here's my last question Philip for you, which is drawing on what you just said.

46:05
That's advice. So you've been through this for many, many years now.

46:10
I'm not sure 10, 20 years. It's a very long time. So you've got a lot of experience in this.

46:16
What would be your advice? There's other educators like you that are having similar issues, similar challenges, I should say.

46:26
Where should they focus their limited resources, especially the limited time if they want to draw students into STEM, engineering in particular?

46:37
I think teamwork is an important aspect of this.

46:41
So with the CSIRO, we've got industry partners which are willing to participate.

46:47
Now, some of the schools don't even bother with STEM clubs and they're struggling teaching STEM as it is.

46:55
But there are resources in the community which they can interact with and get help.

47:03
So what I would advise is that the teachers look at what's available that can support them.

47:11
If they try to do everything themselves, I think it'd be quite hard.

47:15
So when you look at STEM, it's actually got quite a number of components.

47:20
So you need skills in electronics, coding, perhaps even a little bit of mechanical engineering.

47:28
And hardly anyone's got those skills in society, let alone teachers.

47:33
But I think it is possible that you can get that additional help.

47:38
Now that help can come from very keen students that are willing to help.

47:44
And some of the students will know more about coding than the teachers.

47:49
You want to be able to harness all of that rather than trying to do everything yourself.

47:56
So use as much external help as possible that's your number one piece of advice, right?

48:01
Whether it's people, students, equipment, existing, I guess, kids.

48:08
You mentioned the three-minute building time for a robot, I guess, that comes as almost finished robot kit that you're using in your class.

48:18
Use as much of that as possible than try to do everything yourself.

48:21
That's right. And use experiences from other people that might have done similar things.

48:28
So I always search the internet to see, has anyone else done this?

48:32
And I'm a member of STEM education in the UK and other organizations.

48:38
I do a lot of working with China, for example, and I get feedback on projects that I'm working on.

48:46
So I've got some confidence that some of the things I'm working on could work because I've had conversations with other people.

48:54
Right, right. Yeah, someone has done it before, so you know, it works.

48:59
So you're not going to waste your time attempting something that you don't have any evidence.

49:03
It's never worked before.

49:05
That's right.

49:06
But sometimes there's an environment called littleBits electronics, which sometimes is not very reliable.

49:14
And when you're in situations like that, you want to create a positive environment for the students, because if they don't work, well, then they will lose confidence.

49:25
But then if they don't work, you can actually say, well, let's see if we can find out what's going wrong.

49:31
So you have to have enough diagnostic tools.

49:34
So I have tiny little volt meters and things like that, which I can put into a circuit.

49:39
And I can identify if there's a problem in that part of the circuit.

49:44
Yeah, there's no such thing as a failed experiment, right?

49:48
It's always an opportunity to learn.

49:51
Why doesn’t work?

49:52
What did they learn from it?

49:53
I always ask the students right at the end, what did you learn from your experiences?

50:00
And I listen to that.

50:01
I'm sort of interested in what they're saying.

50:04
Okay.

50:05
Well, I think we can live it at this, Philip.

50:08
Thank you very much for coming on this morning here in Sydney, it's midday almost, to tell us about your projects.

50:16
I'd like to follow up with you, say in a few months and see how things are going, especially your STEM recycling project.

50:21
Is there anything else that you'd like to say before we finish up?

50:26
Well, perhaps working with others, for example, with Blacktown environment, I'm now talking to the council, the environmental engineers.

50:35
Wow, okay.

50:36
And so they're interested in extending this to the community.

50:41
And I think there will be surprises.

50:45
So with Blacktown, I'm working with the libraries, but the libraries talk to other people in the councils.

50:53
Yeah.

50:53
And you gradually build up a network.

50:57
And I think that's quite interesting.

50:59
And I try to extend that to the universities to find out, well, are they doing similar things and feed that back into the program.

51:08
Just one step at a time and you actually are increasing the scope of your work cause now you’re connecting with many more people.

51:15
And therefore your work becomes a lot more networked.

51:18
That's right. Exactly.

51:19
You are getting the network effect benefits there.

51:22
Exactly.

51:23
Great.

51:24
Well thank you again, Philip.

51:26
It's been a pleasure as always.

51:28
And we'll be back.

51:30
Oh, thanks for the opportunity.

51:31
I enjoyed the conversation.