This week James and Carrie Anne go outdoors to explore some of the ways in which educators can connect their learners with nature using technology. Whether for investigating local habitats and wildlife or exploring remote locations, technology is a vital tool for learning about the natural world and our place in it. Nature is yet another context through which learners can experience computing concepts and learn about the relevance of programming, physical computing, and machine learning.
Full show notes:
https://helloworld.raspberrypi.org/articles/how-can-computing-help-us-re-connect-with-nature
This week James and Carrie Anne go outdoors to explore some of the ways in which educators can connect their learners with nature using technology. Whether for investigating local habitats and wildlife or exploring remote locations, technology is a vital tool for learning about the natural world and our place in it. Nature is yet another context through which learners can experience computing concepts and learn about the relevance of programming, physical computing, and machine learning.
Full show notes:
https://helloworld.raspberrypi.org/articles/how-can-computing-help-us-re-connect-with-nature
1
00:00:00,810 --> 00:00:02,990
Carrie Anne:
Have you ever gone really wild?
2
00:00:02,990 --> 00:00:05,520
Alasdair Davies:
Strapping Raspberry Pis to
poles in Antarctica and I have
3
00:00:05,520 --> 00:00:07,920
been known to stick them on sea
turtles as well!
4
00:00:07,920 --> 00:00:12,140
James Robinson:
Using technology to connect our
students with frontiers and
5
00:00:12,150 --> 00:00:14,700
settings that would otherwise
be beyond them.
6
00:00:15,060 --> 00:00:17,340
Natalie Shersby:
They'll share with us what
they'd captured. They were so
7
00:00:17,340 --> 00:00:18,540
excited. It's just amazing.
8
00:00:21,460 --> 00:00:24,880
Carrie Anne:
Welcome to Hello World, a
podcast for educators interested
9
00:00:24,880 --> 00:00:27,880
in computing and digital
making, I'm Carrie Anne Philbin
10
00:00:27,880 --> 00:00:31,660
a computing educator, content
creator, YouTuber and Nature
11
00:00:31,660 --> 00:00:32,140
Nurturer.
12
00:00:32,140 --> 00:00:34,420
James Robinson:
And I'm James Robinson a
computing educator.
13
00:00:34,540 --> 00:00:36,640
And I currently work on
projects promoting effective
14
00:00:36,640 --> 00:00:38,070
pedagogy within our subject.
15
00:00:38,470 --> 00:00:41,110
If you want to support our show
and subscribe whenever you get
16
00:00:41,110 --> 00:00:43,600
your podcasts and leave us a
five star review.
17
00:00:43,840 --> 00:00:47,620
Carrie Anne:
Today, we are reconnecting with
nature as we explore ways to
18
00:00:47,620 --> 00:00:50,620
bring the outside into our
computing classrooms.
19
00:00:50,950 --> 00:00:53,890
Not always the most obvious
marriage, technology and nature.
20
00:00:53,900 --> 00:00:57,130
I mean, electronic devices
don't always like our weather,
21
00:00:57,130 --> 00:00:58,880
particularly here in the UK.
22
00:00:59,230 --> 00:01:02,470
However, technology can help
environmentalist learn more
23
00:01:02,470 --> 00:01:05,260
about our planet and the
creatures that inhabit it.
24
00:01:05,560 --> 00:01:09,010
And as we've learnt from
previous episodes, context can
25
00:01:09,010 --> 00:01:12,320
play an important role in
inspiring our students.
26
00:01:12,700 --> 00:01:16,210
So James, have you ever gone
really wild with a computing
27
00:01:16,210 --> 00:01:16,600
project?
28
00:01:17,890 --> 00:01:20,650
James Robinson:
Yeah, I think nature's a really
interesting context.
29
00:01:20,660 --> 00:01:23,830
I think it's something that's
so, so all sort of naturally
30
00:01:23,830 --> 00:01:27,100
connected to and really sort of
tangible sort of context to work
31
00:01:27,100 --> 00:01:30,010
in. But it is one that I've
often struggled to bring into my
32
00:01:30,010 --> 00:01:31,180
classroom in my learning
spaces.
33
00:01:31,570 --> 00:01:34,060
But I think just a couple of
things that I have tried.
34
00:01:34,090 --> 00:01:37,130
I really enjoy doing sort of
time lapse photography so
35
00:01:37,150 --> 00:01:38,290
automated time lapse.
36
00:01:39,280 --> 00:01:42,970
And that's a great thing to do,
whether that be with with plants
37
00:01:42,970 --> 00:01:44,460
or with other creatures.
38
00:01:44,470 --> 00:01:49,420
I think what's really nice
there is that you can see and
39
00:01:49,420 --> 00:01:54,130
visualise and capture processes
that otherwise happen so slowly
40
00:01:54,310 --> 00:01:56,950
that you wouldn't be able to
observe them, you know, just
41
00:01:56,950 --> 00:01:57,720
sitting there watching.
42
00:01:57,970 --> 00:02:01,480
Carrie Anne:
I vividly remember a time when
we first started to work
43
00:02:01,480 --> 00:02:06,490
together where we decided to do
a time lapse of cress growing
44
00:02:06,490 --> 00:02:07,990
inside little eggshells.
45
00:02:07,990 --> 00:02:09,970
And we even like drew little
faces on them.
46
00:02:09,970 --> 00:02:11,740
And we named them James and
Carry Anne.
47
00:02:11,890 --> 00:02:13,780
And you failed to water yours.
48
00:02:14,140 --> 00:02:16,510
And I made sure mine lived.
49
00:02:16,660 --> 00:02:20,140
And there is a really good time
lapse that happens really fast
50
00:02:20,140 --> 00:02:23,350
across 30 seconds where you
just see mine grow and yours
51
00:02:23,350 --> 00:02:24,230
grow and then die.
52
00:02:25,120 --> 00:02:28,550
James Robinson:
Well, I was going for, like,
accuracy, right.
53
00:02:28,580 --> 00:02:32,050
You know, you've got a lot
longer hair mines gradually
54
00:02:32,200 --> 00:02:33,970
disappearing. So I was going
for accuracy.
55
00:02:35,200 --> 00:02:37,660
But yeah, I've done the same
kind of experiment with my
56
00:02:37,840 --> 00:02:39,370
actually my own children at
home.
57
00:02:39,670 --> 00:02:44,890
We grew some tomato plants and
captured the video of those.
58
00:02:45,160 --> 00:02:47,800
And then we were able to
observe photo tropism, which is
59
00:02:47,800 --> 00:02:50,380
the process where plants
gradually kind of angle
60
00:02:50,380 --> 00:02:52,570
themselves towards the light
and grow towards it.
61
00:02:52,570 --> 00:02:55,930
And that's something you can't
really observe in a moment by
62
00:02:55,930 --> 00:02:57,330
moment kind of observation.
63
00:02:57,610 --> 00:02:59,890
So I think it's it's really
it's a really interesting and
64
00:02:59,890 --> 00:03:01,930
important context.
65
00:03:01,930 --> 00:03:04,960
And the other end of that in
terms of being able to use
66
00:03:04,960 --> 00:03:08,290
technology, is we can use it to
observe processes that where
67
00:03:08,290 --> 00:03:12,250
maybe the things we're
observing are quite remote or
68
00:03:12,700 --> 00:03:14,920
actually the processes that
we're trying to observe are
69
00:03:14,920 --> 00:03:17,980
quite delicate and a human
presence there would actually be
70
00:03:17,980 --> 00:03:21,610
quite disruptive. So we can use
technology to have a kind of a
71
00:03:21,610 --> 00:03:23,560
physical presence of
observation for that sort of
72
00:03:23,590 --> 00:03:27,640
space. But without us kind of
being there, I really think it's
73
00:03:27,640 --> 00:03:29,500
really important for our young
learners, as you mentioned,
74
00:03:29,500 --> 00:03:32,590
context just a moment ago, you
know, it's just yet another
75
00:03:32,590 --> 00:03:37,180
really important context where
we can offer some variety and
76
00:03:37,180 --> 00:03:40,300
diversity of experiences to our
students of computing.
77
00:03:40,600 --> 00:03:43,390
And I think also it's
particularly relevant now as the
78
00:03:43,390 --> 00:03:46,780
young people that we are
educating now, there are so many
79
00:03:47,170 --> 00:03:50,650
sort of relevant and important
real world challenges that are
80
00:03:50,650 --> 00:03:54,160
both pressing and interesting
and engaging and very solvable
81
00:03:54,160 --> 00:03:56,640
and by, with use of technology.
82
00:03:56,650 --> 00:04:00,170
So I think it's a really
important context for our for
83
00:04:00,170 --> 00:04:02,450
our learners. You've kind of
often struck me, you do lots of
84
00:04:02,450 --> 00:04:04,250
crafty things. You often
struck me as a bit of an
85
00:04:04,250 --> 00:04:06,900
indoors-y kind of person Carrie
Anne, have.
86
00:04:07,090 --> 00:04:10,210
What have you ever done to
connect computing with nature
87
00:04:10,210 --> 00:04:11,180
in your own experience?
88
00:04:13,480 --> 00:04:15,320
Carrie Anne:
Yeah, I think you've been really
kind there, James.
89
00:04:15,340 --> 00:04:18,100
I think what you're trying to
say is that I'm not very green
90
00:04:18,100 --> 00:04:22,480
fingered and I'm, you know,
more tended to find me in hotels
91
00:04:22,480 --> 00:04:24,430
rather than camping.
92
00:04:25,630 --> 00:04:28,240
I think is what we're saying is
I'm probably not quite as
93
00:04:28,240 --> 00:04:32,860
outdoorsy as I probably should
be, but I am a human being of a
94
00:04:32,860 --> 00:04:37,330
generation that is conscious of
the impact they're having on the
95
00:04:37,330 --> 00:04:41,710
world around them and on the
environment and on the wildlife
96
00:04:41,710 --> 00:04:43,380
that lives on our planet with
us.
97
00:04:44,530 --> 00:04:48,220
I grew up in a very urban
environment, and this is going
98
00:04:48,220 --> 00:04:49,990
to be quite the newsflash for
you.
99
00:04:50,500 --> 00:04:54,310
But when I move to the
countryside, it turns out there
100
00:04:54,310 --> 00:04:58,180
is more wildlife and more
diversity of species of wildlife
101
00:04:58,570 --> 00:04:59,710
in the countryside.
102
00:04:59,710 --> 00:05:01,610
Who knew? Who knew?
103
00:05:01,610 --> 00:05:03,400
I feel like I've discovered
something there and I'm sharing
104
00:05:03,400 --> 00:05:08,380
it with you all. But what I
mean by that is I am more aware
105
00:05:08,380 --> 00:05:12,880
of the animals in the wildlife
that's in my locality, and that
106
00:05:12,880 --> 00:05:18,640
is having an impact on how I
perhaps do my gardening or how I
107
00:05:19,210 --> 00:05:22,830
create an environment that's
not just suitable for me, but is
108
00:05:22,830 --> 00:05:26,310
also suitable for the wildlife
that clearly wants to live
109
00:05:26,310 --> 00:05:29,370
alongside us, wherever that's
in the planting that I put in my
110
00:05:29,370 --> 00:05:34,070
garden or the spaces that I
create for that for that
111
00:05:34,080 --> 00:05:36,810
wildlife to get in and live.
112
00:05:36,810 --> 00:05:39,360
So, for example, we have a lot
of birds that come into our
113
00:05:39,360 --> 00:05:43,470
garden. And every year, year on
year, the number of nesting
114
00:05:43,470 --> 00:05:45,060
birds in my garden is just
crazy.
115
00:05:45,570 --> 00:05:49,740
As soon as one nest vacates,
another group of birds move in.
116
00:05:49,740 --> 00:05:50,940
Or maybe it's the same.
117
00:05:51,450 --> 00:05:54,050
I'm just not knowledgeable
enough to know.
118
00:05:54,060 --> 00:05:57,210
But if I start using technology
to track that a little bit
119
00:05:57,210 --> 00:06:01,200
better, maybe I would be much
more knowledgeable about what
120
00:06:01,200 --> 00:06:05,160
that wildlife needs to thrive
in that environment and what
121
00:06:05,160 --> 00:06:06,680
more I could be doing to
support it.
122
00:06:08,010 --> 00:06:10,860
One of the really big
mindblowing moments for me was
123
00:06:11,340 --> 00:06:14,370
last year I woke up in the
night and I went downstairs to
124
00:06:14,370 --> 00:06:16,610
my kitchen to get a drink in
the middle of the night.
125
00:06:17,240 --> 00:06:20,940
We've got a security camera in
our garden that goes off when
126
00:06:20,940 --> 00:06:24,330
motion is detected and quite
often it can go off just
127
00:06:24,330 --> 00:06:27,290
anything sets it off, a tree
moving, whatever.
128
00:06:27,750 --> 00:06:30,150
So I went downstairs to get a
drink in the dark and suddenly
129
00:06:30,150 --> 00:06:31,490
the security light went off.
130
00:06:31,500 --> 00:06:34,550
So it was like, oh, no,
intruder alert, intruder alert.
131
00:06:34,860 --> 00:06:38,040
And I looked outside the window
and there was the most beautiful
132
00:06:38,040 --> 00:06:42,350
hedgehog you've ever seen, just
rumbling around in my garden,
133
00:06:42,810 --> 00:06:44,250
heading for the Bug Hotel.
134
00:06:44,250 --> 00:06:46,110
So sorry, insects.
135
00:06:46,110 --> 00:06:49,440
But I think I think you when
you were on course to be dinner.
136
00:06:50,160 --> 00:06:52,670
But for me, I've never seen a
wild hedgehog.
137
00:06:52,950 --> 00:06:54,090
For me that was sort of mind
blowing.
138
00:06:54,510 --> 00:06:56,730
I told my mom about it and she
was just like, oh, my God,
139
00:06:56,730 --> 00:07:02,070
that's amazing. But, you know,
that sent me on a journey about
140
00:07:02,640 --> 00:07:06,060
what was it about the habitat
that meant that the hedgehogs
141
00:07:06,060 --> 00:07:07,380
were coming into my garden.
142
00:07:07,590 --> 00:07:09,600
What could I be doing to
support that better?
143
00:07:09,610 --> 00:07:12,870
What little spaces could I
create to kind of encourage that
144
00:07:13,080 --> 00:07:14,430
. Was it unusual, actually.
145
00:07:14,430 --> 00:07:16,170
Should I be worried about that
hedgehog?
146
00:07:16,740 --> 00:07:20,010
And should I be contacting
someone to to make sure that
147
00:07:20,250 --> 00:07:21,360
hedgehog's alright.
148
00:07:21,360 --> 00:07:24,960
If I had set up some technology
because I'm not awake in the
149
00:07:24,960 --> 00:07:28,620
night, like you said, James,
earlier on, I could be finding
150
00:07:28,620 --> 00:07:31,290
out more about the life cycle
of that hedgehog.
151
00:07:31,590 --> 00:07:31,890
Right.
152
00:07:32,820 --> 00:07:34,650
James Robinson:
I think it's really interesting
as well. I was reading a thing
153
00:07:34,650 --> 00:07:38,910
recently about how as humans
spread across the world, it's
154
00:07:38,910 --> 00:07:43,140
not just we inhabit areas, but
we actually we break up what we
155
00:07:43,140 --> 00:07:44,400
demark the land.
156
00:07:44,400 --> 00:07:46,830
And so actually we're kind of
creating barriers between
157
00:07:47,040 --> 00:07:48,450
different species that interact
normally.
158
00:07:48,870 --> 00:07:52,860
And so you see the species
diminish in that area and the
159
00:07:52,870 --> 00:07:56,490
sort of the variety of nature
kind of just dwindles a little
160
00:07:56,490 --> 00:08:00,390
bit because there are these
human barriers between their
161
00:08:00,390 --> 00:08:01,090
interactions.
162
00:08:01,130 --> 00:08:03,600
Carrie Anne:
Well, maybe we're more
adventurous and more
163
00:08:03,600 --> 00:08:06,270
knowledgeable maybe about this
topic than we've given
164
00:08:06,270 --> 00:08:08,610
ourselves credit for. I think
we're about to find out.
165
00:08:08,970 --> 00:08:11,520
I mean, when I think about
technology in the wild, I
166
00:08:11,520 --> 00:08:16,080
immediately think of our first
guest, an active conservationist
167
00:08:16,080 --> 00:08:18,870
and technologist who I met many
years ago when he started
168
00:08:19,050 --> 00:08:22,800
strapping Raspberry Pis to
Poles in Antarctica to conduct
169
00:08:23,040 --> 00:08:26,250
field research. So welcome
technical director of the
170
00:08:26,250 --> 00:08:29,130
Arribada Initiative, co-founder
of NatureBytes, Shuttleworth
171
00:08:29,130 --> 00:08:31,470
Foundation Fellow and good
friend to the Raspberry Pi
172
00:08:31,470 --> 00:08:32,880
Foundation Alasdair Davies,
welcome.
173
00:08:33,720 --> 00:08:34,820
Thank you for joining us.
174
00:08:35,250 --> 00:08:36,960
What is a conservation
technologist?
175
00:08:37,500 --> 00:08:39,330
Sounds like the best job title
in the world.
176
00:08:39,600 --> 00:08:40,720
And what do you do?
177
00:08:41,220 --> 00:08:42,330
Alasdair Davies:
Well, welcome. Thank you very
much.
178
00:08:43,620 --> 00:08:48,240
Conservation technologist is a
really exciting job to have
179
00:08:48,510 --> 00:08:52,080
because your goal really is to
help scientists solve their
180
00:08:52,080 --> 00:08:56,100
problems. And as you were
saying moments ago, that is
181
00:08:56,100 --> 00:09:00,330
often around understanding
behaviour of animals, using
182
00:09:00,330 --> 00:09:03,960
various sensors to understand
environmental change over time.
183
00:09:04,500 --> 00:09:08,580
And what's exciting to me is I
spent about 11 years at a South
184
00:09:08,580 --> 00:09:10,020
London Zoo doing just that.
185
00:09:10,500 --> 00:09:13,050
And you're right, I was
dropping Raspberry Pis to Poles
186
00:09:13,050 --> 00:09:15,960
in Antarctica and I have been
known to stick them on sea
187
00:09:15,960 --> 00:09:19,590
turtles as well and use the
camera to look at behaviour.
188
00:09:20,190 --> 00:09:23,490
And for me, it's been really
exciting because I have, I
189
00:09:23,490 --> 00:09:27,720
think, merged the boundary
between how scientists observe
190
00:09:27,720 --> 00:09:29,640
the world and how you get that
into the classroom.
191
00:09:29,940 --> 00:09:32,340
So it's quite exciting for me
today to be here with you to
192
00:09:32,340 --> 00:09:34,890
kind of share some tips on what
that's been like for me.
193
00:09:35,550 --> 00:09:39,840
James Robinson:
So what first inspired you to
become involved in conservation?
194
00:09:40,560 --> 00:09:44,920
Alasdair Davies:
When I was growing up, about 18,
19 years old, I went i
195
00:09:44,920 --> 00:09:46,890
nter-railing. Not sure if any
of you've heard of
196
00:09:47,460 --> 00:09:48,490
inter-railing, but I was.
197
00:09:49,010 --> 00:09:51,870
Carrie Anne:
Yeah. That's a generational
thing right there.
198
00:09:52,200 --> 00:09:54,690
Alasdair Davies:
Yeah. It's like that was the
thing to do.
199
00:09:54,840 --> 00:09:58,320
You get your ticket, the golden
ticket and you hit the rails and
200
00:09:58,320 --> 00:10:02,370
off you went. And when I did
that, it was quite eye opening
201
00:10:02,370 --> 00:10:04,110
to just, the change in the
world today.
202
00:10:04,380 --> 00:10:07,350
So the beaches I was visiting
in, say, Barcelona, there was
203
00:10:07,520 --> 00:10:09,750
little flecks of different
colours on the sand, which are
204
00:10:10,020 --> 00:10:12,270
now very well know as micro
plastics.
205
00:10:12,270 --> 00:10:14,130
Whereas back then, it was
something it was really just
206
00:10:14,400 --> 00:10:15,480
bubbling up to the surface.
207
00:10:16,440 --> 00:10:18,570
My trips abroad to rainforests.
208
00:10:18,780 --> 00:10:21,640
Once you're in, that world, in
that zone of experience that I
209
00:10:21,640 --> 00:10:25,390
think is so captivating, you
want to do what you can to help
210
00:10:25,390 --> 00:10:30,130
conserve it, share it for other
generations, and as a kind of
211
00:10:30,730 --> 00:10:33,790
electrical engineer as well and
interested in computing, I
212
00:10:33,790 --> 00:10:37,430
merged the two worlds together
and crafted this this term
213
00:10:37,450 --> 00:10:41,260
conservation technologist, but
there's been a real push over
214
00:10:41,260 --> 00:10:44,920
the last few years looking at
how this drive in low cost
215
00:10:44,920 --> 00:10:49,210
electronics and access can
really make a difference.
216
00:10:49,210 --> 00:10:52,060
You know, what we're doing
today was hard to do 10 years
217
00:10:52,060 --> 00:10:55,060
ago, but the advent of like
Pico and all these really low
218
00:10:55,060 --> 00:10:58,960
cost microcontrollers and the
Pi itself, there's been so much
219
00:10:58,960 --> 00:11:01,030
we can do in this world to
really understand the
220
00:11:01,030 --> 00:11:04,230
environment. And that's what
I've been doing, just harnessing
221
00:11:04,240 --> 00:11:07,510
that that access to those
electronics and repurposing them
222
00:11:07,750 --> 00:11:08,950
for conservation goals.
223
00:11:09,130 --> 00:11:13,060
Carrie Anne:
And how has technology impacted
the field more generally in
224
00:11:13,060 --> 00:11:14,280
conservation?
225
00:11:14,320 --> 00:11:18,070
Alasdair Davies:
Oh it's been massive. I would
say, for example, this is
226
00:11:18,070 --> 00:11:21,190
bubbling up too but tiny ML, so
tiny machine learning.
227
00:11:21,760 --> 00:11:26,890
That's where you're teaching
essentially the computer to look
228
00:11:26,890 --> 00:11:27,970
for specific objects.
229
00:11:28,000 --> 00:11:30,790
So let's say a polar bear is a
project I'm working on the
230
00:11:30,790 --> 00:11:33,490
minute where we're using
thermal cameras to detect the
231
00:11:33,490 --> 00:11:36,790
silhouettes of polar bears,
we're teaching the camera, which
232
00:11:36,790 --> 00:11:39,910
will be a Raspberry Pi 4
exactly what a polar bear looks
233
00:11:39,910 --> 00:11:42,850
like in thermal vision,
training that model and doing it
234
00:11:42,850 --> 00:11:44,980
all on the chip, all on the
computer itself.
235
00:11:45,430 --> 00:11:48,400
And if you think about how you
can take something exciting as
236
00:11:48,400 --> 00:11:51,430
working with a polar bear and
place it in the classroom, you
237
00:11:51,430 --> 00:11:52,990
can retrain that to detect
people.
238
00:11:53,650 --> 00:11:56,410
You can have it as a person
detector, someone walking in and
239
00:11:56,410 --> 00:11:59,380
out of the room. You can put it
in your garden and look for
240
00:11:59,380 --> 00:12:02,110
foxes by training it to look
for silhouettes of foxes.
241
00:12:02,290 --> 00:12:05,200
Or you could even find that
wonderful hedgehog that you
242
00:12:05,200 --> 00:12:09,010
spotted and you could teach
your Raspberry Pi to detect it
243
00:12:09,010 --> 00:12:13,270
in thermal vision. So that just
just shows you kind of like what
244
00:12:13,270 --> 00:12:14,470
we're doing in the conservation
world.
245
00:12:15,220 --> 00:12:16,420
Carrie Anne:
I'm super interested.
246
00:12:16,420 --> 00:12:17,410
You've piqued my interest now.
247
00:12:17,410 --> 00:12:19,450
James Robinson:
I've got project envy.
248
00:12:20,620 --> 00:12:22,570
Carrie Anne:
I mean, it sounds amazing.
249
00:12:22,960 --> 00:12:25,240
Are there any specific projects
that you've worked for?
250
00:12:25,250 --> 00:12:28,080
You talked a bit about the
turtles and strapping things,
251
00:12:28,420 --> 00:12:29,740
devices to turtles.
252
00:12:29,840 --> 00:12:32,920
It'll be great to kind of hear
some of those stories for our
253
00:12:32,920 --> 00:12:36,650
teachers to be able to to
understand this, this world,
254
00:12:36,680 --> 00:12:37,710
this landscape a bit more.
255
00:12:38,230 --> 00:12:43,030
Alasdair Davies:
What I do on a day to day basis
is correspond with a an after
256
00:12:43,030 --> 00:12:47,440
school computing club, which
Arribada set up on a West
257
00:12:47,440 --> 00:12:50,020
African island called
Principe, so a population of
258
00:12:50,020 --> 00:12:51,610
only a few thousand people.
259
00:12:52,420 --> 00:12:54,850
And after school, the local
kids go down to the club and we
260
00:12:54,850 --> 00:12:57,450
give them free access to
computing, education, STEM
261
00:12:57,460 --> 00:13:01,870
activities. And what I have
found fascinating, and I really
262
00:13:01,870 --> 00:13:04,930
hope this will kind of become
more apparent in the classroom,
263
00:13:04,930 --> 00:13:10,210
is with the opportunity now to
stream content or create
264
00:13:10,210 --> 00:13:13,640
podcasts and access to
Internet, things like Starlink
265
00:13:13,810 --> 00:13:16,210
that are coming up. You know,
Fastnet from anywhere.
266
00:13:17,210 --> 00:13:20,120
We've been linking the
classroom in Principe to
267
00:13:20,210 --> 00:13:24,620
classrooms abroad and having
the kids exchange activities and
268
00:13:24,620 --> 00:13:27,140
what's become apparent in
nature conservation is that you
269
00:13:27,140 --> 00:13:30,320
then have access to this
faraway island, this land where
270
00:13:30,410 --> 00:13:35,470
you can't get to physically
becomes open to innovation.
271
00:13:35,480 --> 00:13:39,410
So you can have children decide
how to build a time lapse camera
272
00:13:39,680 --> 00:13:44,570
in Peckham, share their
insights with the kids in
273
00:13:44,570 --> 00:13:47,630
Principe, the kids in Principe
build it, put it out on the
274
00:13:47,630 --> 00:13:51,050
island, share their results,
and the kids in Peckham get to
275
00:13:51,050 --> 00:13:53,030
see these fantastic species.
276
00:13:54,020 --> 00:13:56,600
And when I started in this
world, we weren't really doing
277
00:13:56,600 --> 00:13:59,630
that. We were still going to
the school garden or taking kits
278
00:13:59,630 --> 00:14:02,090
home, which is still
fascinating and great to get
279
00:14:02,090 --> 00:14:05,660
involved in. So you get those,
you know, the native species,
280
00:14:05,660 --> 00:14:06,830
which is still eye opening.
281
00:14:07,340 --> 00:14:10,580
But I think with that, what's
next question to really excite
282
00:14:10,580 --> 00:14:14,540
the kids, it's saying, hey,
let's do another Skype call
283
00:14:14,540 --> 00:14:16,580
because we're going to see what
your cameras got.
284
00:14:17,000 --> 00:14:20,690
And that, to me, is a new
boundary in what future
285
00:14:20,690 --> 00:14:23,400
activities could look like
we've been experimenting with at
286
00:14:23,420 --> 00:14:24,680
the minute. So just dabbling in
it.
287
00:14:24,800 --> 00:14:27,460
We've done a few links up with
various zoos and so on.
288
00:14:27,770 --> 00:14:30,110
There's always a language
barrier issue, that we have to
289
00:14:30,110 --> 00:14:33,920
break down. But again, even
through language there's some
290
00:14:33,920 --> 00:14:36,830
really great translation
services now you can get videos,
291
00:14:37,430 --> 00:14:40,730
you can run them through Google
Voice or whatever, and we can
292
00:14:40,730 --> 00:14:43,910
just really push the boundaries
there and say, well, why can't
293
00:14:43,910 --> 00:14:49,010
we link a school classroom in
the Arctic to a school classroom
294
00:14:49,010 --> 00:14:50,610
in Norwich? And we can.
295
00:14:51,020 --> 00:14:54,380
So I really hope we do more of
that moving forwards and try to
296
00:14:54,740 --> 00:14:58,210
share these nature experiments
in that way.
297
00:14:58,550 --> 00:15:00,500
Carrie Anne:
I think it kind of goes back to
what you were talking about with
298
00:15:00,500 --> 00:15:01,910
your inter-railing experience.
299
00:15:01,910 --> 00:15:04,940
It's almost like being able to
take young people who perhaps
300
00:15:04,940 --> 00:15:08,060
can't, you know, don't have
access to the funds to be able
301
00:15:08,060 --> 00:15:10,550
to travel around the world,
but can still find ways to
302
00:15:10,550 --> 00:15:13,200
connect them with the
environments and the people of
303
00:15:13,430 --> 00:15:17,090
of those other countries and
vice versa, I guess.
304
00:15:17,480 --> 00:15:20,810
Alasdair Davies:
Yeah, definitely. And the
excitement from sharing the
305
00:15:20,810 --> 00:15:21,950
content is key.
306
00:15:22,130 --> 00:15:26,090
If you can put up some really
captivating content even before
307
00:15:26,120 --> 00:15:30,890
you engage your class in the in
the making part of it, I think
308
00:15:30,890 --> 00:15:33,170
it reinforces the fact that
it's worth putting your energy
309
00:15:33,170 --> 00:15:34,240
into to do.
310
00:15:34,240 --> 00:15:37,310
So say you've had a really long
school day, you then get stuck
311
00:15:37,310 --> 00:15:40,130
into an activity, something
goes wrong, someone's having
312
00:15:40,640 --> 00:15:43,310
trouble getting their code
running or they've put their
313
00:15:43,310 --> 00:15:44,600
jumper cables on back to front.
314
00:15:44,600 --> 00:15:45,650
You haven't spotted it.
315
00:15:45,650 --> 00:15:48,710
And they're a little bit kind
of disillusioned starting the
316
00:15:48,710 --> 00:15:52,010
day off with here's what here's
what scientists do with the same
317
00:15:52,010 --> 00:15:53,330
hardware you're touching is
key.
318
00:15:54,920 --> 00:15:57,740
And one really nice story I can
share with you, which is a world
319
00:15:57,740 --> 00:16:02,630
exclusive, coming to a
Raspberry Pi blog shortly, is in
320
00:16:02,630 --> 00:16:07,340
2018 before the world changed
pre pandemic, when everything
321
00:16:07,340 --> 00:16:09,050
was normal, as we called it.
322
00:16:09,800 --> 00:16:13,100
I was lucky enough to travel to
Antarctica, like you said, but
323
00:16:13,100 --> 00:16:14,420
we stuck a new camera out.
324
00:16:14,480 --> 00:16:16,220
It's a new build. We hadn't
done it before.
325
00:16:16,400 --> 00:16:18,260
And we put a PI zero inside.
326
00:16:18,620 --> 00:16:21,600
So the cheapest, most
accessible one at the time.
327
00:16:21,620 --> 00:16:23,800
Pre-Pico and everything.
328
00:16:23,800 --> 00:16:27,500
Put a solar panel on it and we
ran it for what we thought would
329
00:16:27,500 --> 00:16:30,110
be a year. So we thought it
would run for a year and it was
330
00:16:30,110 --> 00:16:34,430
on a timelapse rotation
watching over a penguin colony
331
00:16:34,430 --> 00:16:36,170
an Adelie penguin colony.
332
00:16:36,170 --> 00:16:38,540
It was on the top of a volcano
in Antarctica.
333
00:16:38,870 --> 00:16:41,540
It's very strange, but penguins
like to like volcanoes
334
00:16:41,560 --> 00:16:43,250
Antarctica, it's all a little
bit warmer for them.
335
00:16:43,760 --> 00:16:47,000
But we put it out for a year in
2018 and we thought we'd get it
336
00:16:47,000 --> 00:16:48,080
back in two thousand ninety.
337
00:16:48,440 --> 00:16:52,130
But the sea ice was too thick
in 2019 for the boat to get
338
00:16:52,130 --> 00:16:55,250
access. So we lost the year so
it had to stay out for two years
339
00:16:55,830 --> 00:16:57,200
. I'm Sitting thinking oh my
gosh, I wonder if this things
340
00:16:57,200 --> 00:16:59,210
going to get through two
Antarctic winters.
341
00:16:59,810 --> 00:17:04,600
So 2019 goes by 2020 hits and
the pandemic starts, it's all
342
00:17:04,610 --> 00:17:07,520
about to cancel. The whole
world changes and our little
343
00:17:07,520 --> 00:17:09,350
camera is still out there on
the ice.
344
00:17:09,920 --> 00:17:11,720
So we had to leave it out there
for three years.
345
00:17:12,200 --> 00:17:14,600
And then Tom, the
Penguinologist, which is a great
346
00:17:14,600 --> 00:17:18,500
job title, I didn't get told at
school that you could become a
347
00:17:18,500 --> 00:17:20,120
penguinologist, but you can be
.
348
00:17:21,760 --> 00:17:24,980
He got he got his hands on it
last month and he brought it
349
00:17:24,980 --> 00:17:27,230
home and it had been out there
for three and a half years on
350
00:17:27,230 --> 00:17:31,600
the ice, this little Raspberry
Pi Zero in its little protective
351
00:17:31,610 --> 00:17:33,260
enclosure. And I popped it out.
352
00:17:33,470 --> 00:17:36,890
Took the SD card out and took
the brave moment of clicking
353
00:17:37,550 --> 00:17:39,500
camera folder to see what it
had saved.
354
00:17:39,680 --> 00:17:43,500
And it was sitting there for a
while. Whirring away, and I
355
00:17:43,500 --> 00:17:44,810
though oh it's taking forever.
356
00:17:44,810 --> 00:17:46,400
It's going to be corrupt.
Something's gone wrong here.
357
00:17:46,670 --> 00:17:50,390
And then it stopped and it said
twenty seven thousand six
358
00:17:50,390 --> 00:17:52,860
hundred and fourteen photos.
359
00:17:52,860 --> 00:17:57,260
And that camera had taken a
photo every hour for three and a
360
00:17:57,260 --> 00:18:00,170
half years, a PI zero.
361
00:18:00,380 --> 00:18:02,350
And I was like, oh my goodness.
362
00:18:02,750 --> 00:18:04,400
And the photos are absolutely
stunning.
363
00:18:04,820 --> 00:18:09,020
It's like the most in-depth
private view of the penguins
364
00:18:09,020 --> 00:18:12,110
world. Amazing data for the
scientists, they have got sea
365
00:18:12,110 --> 00:18:14,930
ice in the background so they
can quantify how ice changed
366
00:18:14,930 --> 00:18:17,180
because they're worried about
climate change on the peninsula
367
00:18:17,580 --> 00:18:21,000
. They've got the penguins
leaving, coming back for three
368
00:18:21,000 --> 00:18:25,800
whole years, seasonality, they
can quantify sea ice movements
369
00:18:25,800 --> 00:18:28,890
as well. So it's all the data
they have dreamed of and we did
370
00:18:28,890 --> 00:18:31,830
it with a Pi Zero. And if you
take these photos of these
371
00:18:31,830 --> 00:18:34,820
penguins like National
Geographic style photos and put
372
00:18:34,840 --> 00:18:38,730
in front of a class of
children, amazingly captivating,
373
00:18:38,910 --> 00:18:40,930
and then you say, hey, you
know, that Zero, that was in
374
00:18:40,930 --> 00:18:43,140
this this camera here, it's on
your desk.
375
00:18:43,140 --> 00:18:44,280
Let's go make cameras.
376
00:18:44,280 --> 00:18:46,350
And there's so much more
engaged because then they think,
377
00:18:46,650 --> 00:18:47,940
wow, I can do this.
378
00:18:48,270 --> 00:18:50,160
So this will all be open
access.
379
00:18:50,610 --> 00:18:53,030
You can get access to the
photos. It's going to go on to a
380
00:18:53,040 --> 00:18:55,200
citizen science site so you can
ID penguins.
381
00:18:56,490 --> 00:18:57,960
That's what I want to see
happen next.
382
00:18:58,170 --> 00:19:01,770
If more scientists can share
their data in that way as well
383
00:19:02,010 --> 00:19:04,770
and get it into classrooms, we
can really captivate these young
384
00:19:04,770 --> 00:19:06,390
audiences and say, you can do
this.
385
00:19:07,470 --> 00:19:11,780
And I'm just so happy that that
famous Pi Zero ran for three
386
00:19:11,780 --> 00:19:13,410
and a half years on the ice. I
mean, what a dream.
387
00:19:13,530 --> 00:19:15,690
James Robinson:
They are really robust things.
388
00:19:15,700 --> 00:19:18,450
I have a similar story, but I'm
not really into it now.
389
00:19:18,450 --> 00:19:20,850
But we recovered one after it
was washed up through came
390
00:19:20,850 --> 00:19:23,580
through the North Sea and
landed on a beach and was sent
391
00:19:23,580 --> 00:19:26,570
back to us. And again, we had
lots of photos though a high
392
00:19:26,570 --> 00:19:28,380
altitude balloon flight. So they
are very robust.
393
00:19:28,380 --> 00:19:31,620
But I think I want to return to
that point about about sort of
394
00:19:31,620 --> 00:19:37,230
using technology to connect our
students with frontiers and
395
00:19:37,230 --> 00:19:41,820
contexts and settings that
would otherwise be beyond them.
396
00:19:42,090 --> 00:19:44,340
And I think that that's back
when I was doing.
397
00:19:44,550 --> 00:19:46,650
I've mentioned high altitude
ballooning and I won't go into
398
00:19:46,650 --> 00:19:48,900
that. But one of the reasons
for choosing a project like that
399
00:19:48,900 --> 00:19:53,100
was I was very literally
expanding the horizons of those
400
00:19:53,100 --> 00:19:57,420
pupils and taking them to a
place that they couldn't go with
401
00:19:57,420 --> 00:20:00,150
some very low cost and
accessible technology.
402
00:20:00,150 --> 00:20:02,550
And I think that this is a
similar kind of thing.
403
00:20:02,700 --> 00:20:06,450
You know, if we can connect
them with these far flung remote
404
00:20:06,450 --> 00:20:10,770
sort of natural landscapes,
then actually we can make those
405
00:20:10,770 --> 00:20:14,610
places more important, more
relevant, more make our students
406
00:20:14,610 --> 00:20:15,730
feel more connected to them.
407
00:20:15,780 --> 00:20:18,480
I think that's a really
important goal and something
408
00:20:18,480 --> 00:20:19,590
that we can we can do through
technology.
409
00:20:20,100 --> 00:20:23,230
So I think, you know, I'm just
in awe of these kind of projects
410
00:20:23,240 --> 00:20:23,940
. It's fantastic.
411
00:20:24,420 --> 00:20:27,000
Carrie Anne:
And I would have to echo those
thoughts.
412
00:20:27,420 --> 00:20:30,840
But but also just draw
attention to the part about data
413
00:20:30,840 --> 00:20:35,820
and collecting data and making
the, the concepts of computer
414
00:20:35,820 --> 00:20:38,910
science relevant by showing
these real world examples.
415
00:20:38,930 --> 00:20:41,970
So we've touched a bit there on
data science and a little bit on
416
00:20:41,970 --> 00:20:45,030
machine learning. And you get a
really great example about how
417
00:20:45,180 --> 00:20:48,780
training a model can help help
with conservation.
418
00:20:49,110 --> 00:20:51,330
And and you're talking about
those penguins in those
419
00:20:51,330 --> 00:20:53,370
photographs. And I was thinking
about all the data you talked
420
00:20:53,370 --> 00:20:55,950
about, about the ice kind of
moving and the birds coming in
421
00:20:55,950 --> 00:20:59,250
and out, whether or not training
a computer to do that
422
00:20:59,250 --> 00:21:01,800
statistical analysis was kind
of part of that journey.
423
00:21:01,800 --> 00:21:04,580
And I just think all these
things just really bring
424
00:21:04,580 --> 00:21:09,570
computer science to life and
away from just binary or just,
425
00:21:09,570 --> 00:21:12,120
you know, what's a neural
network. It kind of really
426
00:21:13,140 --> 00:21:17,520
provides a real world context,
which we always say not every
427
00:21:17,730 --> 00:21:20,760
child is going to become a
software engineer by learning
428
00:21:20,760 --> 00:21:24,030
computer science, but every
young person, they may go into
429
00:21:24,030 --> 00:21:27,960
all of these different worlds,
these different professions,
430
00:21:28,650 --> 00:21:31,830
these different passions that
they have, whether it be animals
431
00:21:31,830 --> 00:21:35,250
whether it be, you know, high
altitude ballooning, whether it
432
00:21:35,250 --> 00:21:39,300
be space and whatever these
things are, create creative
433
00:21:39,300 --> 00:21:41,880
pursuits. Actually, computer
science can play a part in all
434
00:21:41,880 --> 00:21:42,170
of them.
435
00:21:42,180 --> 00:21:44,340
James Robinson:
Yeah, I think this is a really
good point.
436
00:21:44,390 --> 00:21:47,490
I think my my I think my final
question maybe for Alasdair.
437
00:21:47,530 --> 00:21:49,620
I'd be really interested. We've
kind of alluded to it a couple
438
00:21:49,620 --> 00:21:53,400
of times. But what are the ways
in which schools can get
439
00:21:53,400 --> 00:21:56,810
involved in the kinds of
projects that you're doing?
440
00:21:56,820 --> 00:22:00,060
How can they get connected to
the real science that's going
441
00:22:00,060 --> 00:22:02,130
out there, going on out there
in the world?
442
00:22:02,520 --> 00:22:05,130
Alasdair Davies:
Yes, a really good question,
because often you ask where do
443
00:22:05,130 --> 00:22:07,890
you go? Where where's the
portal to this world?
444
00:22:08,100 --> 00:22:11,070
I think what what I'd like to
suggest first is the citizen
445
00:22:11,070 --> 00:22:12,160
science aspect of it.
446
00:22:12,420 --> 00:22:15,690
There are a lot of good citizen
science projects where you can
447
00:22:15,690 --> 00:22:19,560
just open your browser and have
a very easy 30 minute session
448
00:22:19,560 --> 00:22:20,580
with your school class.
449
00:22:20,880 --> 00:22:23,610
And take them to say Zooniverse
where they can engage with the
450
00:22:23,610 --> 00:22:25,620
Penguin project or they can
look at other scientific
451
00:22:25,620 --> 00:22:28,800
projects and that first
introduces them to data.
452
00:22:28,810 --> 00:22:32,640
As you said, that data is
useful, data is used in science,
453
00:22:32,760 --> 00:22:35,250
that it can be fun. Lots of
these activities are kind of
454
00:22:35,250 --> 00:22:38,280
point and click. You know, can
you identify an object in there
455
00:22:38,730 --> 00:22:41,670
and that then is the bridge to
what machine learning does for
456
00:22:41,670 --> 00:22:45,540
us. So, OK, this is taking you
an hour to look at 20 photos.
457
00:22:45,900 --> 00:22:50,530
What's next? And another great
website is Instant Wild, which
458
00:22:50,530 --> 00:22:54,240
is a ZSL project where you get
some live photos and you can ID
459
00:22:54,240 --> 00:22:57,150
the species there? And that's a
really good one to just have a
460
00:22:57,150 --> 00:22:59,670
look that's got a lot of
cameras globally.
461
00:22:59,670 --> 00:23:01,050
And then moving forwards.
462
00:23:01,320 --> 00:23:04,560
The next step, I think it's
something that you touched on a
463
00:23:04,560 --> 00:23:08,640
moment ago. But when we
introduce these young minds to
464
00:23:08,640 --> 00:23:12,420
computing, I, too, believe that
not everybody wants to be a
465
00:23:12,420 --> 00:23:15,900
hardware engineer, not everyone
to be a software engineer.
466
00:23:16,490 --> 00:23:19,190
There are a lot of students who
just love working with data and
467
00:23:19,190 --> 00:23:22,550
presenting, so if you can
actually get them stats and they
468
00:23:22,550 --> 00:23:24,500
can draw up their own
PowerPoint and do their own
469
00:23:24,500 --> 00:23:27,880
charts and actually explain
what's going on with the data,
470
00:23:27,890 --> 00:23:33,410
it's just as impactful than you
having to get into Python code
471
00:23:33,410 --> 00:23:35,780
or muck around in Scratch or
anything else.
472
00:23:37,280 --> 00:23:40,820
So the activities that I've
seen recently that have been a
473
00:23:40,820 --> 00:23:43,490
little bit more accessible,
especially for the younger
474
00:23:43,490 --> 00:23:45,920
minds, have been the ones
where we've said here's some
475
00:23:45,920 --> 00:23:49,600
tracking data from sea turtles,
GPS telemetry data.
476
00:23:49,970 --> 00:23:52,970
You tell us what you think this
turtle is doing, but we don't
477
00:23:52,970 --> 00:23:55,190
give anything away. We just
give them the Excel spreadsheet
478
00:23:55,190 --> 00:23:58,100
with the GPS, lat and long and
then they go and they have to
479
00:23:58,100 --> 00:24:00,470
actually look at the lat and
long to figure out how it even
480
00:24:00,470 --> 00:24:01,840
works, like what am I doing
here?
481
00:24:01,970 --> 00:24:03,620
And they map it on say Google
Earth.
482
00:24:03,980 --> 00:24:06,260
And they come up with, oh,
look, here's a map and then
483
00:24:06,260 --> 00:24:08,660
they'll get a boundary every
day these turtles have been.
484
00:24:09,110 --> 00:24:11,960
And that then gets them into the
world of, oh, what's a marine
485
00:24:11,960 --> 00:24:14,180
protected area? How are you
going to protect your turtle?
486
00:24:14,510 --> 00:24:18,080
And then they will map themself
by your geo boundary and boxes.
487
00:24:18,650 --> 00:24:21,710
And those baby steps are great
because when you then say to
488
00:24:21,710 --> 00:24:23,540
them, here's what's next, we're
going to look at putting a
489
00:24:23,540 --> 00:24:24,590
camera on a sea turtle.
490
00:24:24,590 --> 00:24:25,730
You can make your own camera
again.
491
00:24:26,540 --> 00:24:28,220
They want to get engaged
because they've now been you
492
00:24:28,220 --> 00:24:30,470
know connected to the life of
the sea turtle.
493
00:24:30,800 --> 00:24:33,650
That kind of data is already
accessible for activities on the
494
00:24:33,660 --> 00:24:37,220
Raspberry Pi website, there's
one which you can look at real
495
00:24:37,220 --> 00:24:41,480
data, which I provided of a sea
turtle in Guinea Bissau going
496
00:24:41,480 --> 00:24:45,140
about its life. I'm sure, we
can share it connected to this
497
00:24:45,140 --> 00:24:48,050
podcast. Have a look at that
run that activity with your
498
00:24:48,050 --> 00:24:50,540
school class. Start with the
basic data and stats.
499
00:24:50,560 --> 00:24:53,240
Don't even have to touch the
hardware and then progress into
500
00:24:53,240 --> 00:24:58,010
the more Maker University world
of what can we do and put in our
501
00:24:58,010 --> 00:24:59,330
garden from today.
502
00:24:59,330 --> 00:25:01,460
That's my kind of tip to say
try it.
503
00:25:01,670 --> 00:25:04,460
I've seen it work and there's a
lot of interest to do that.
504
00:25:04,590 --> 00:25:06,800
James Robinson:
That's really interesting. And
two great tips there.
505
00:25:06,800 --> 00:25:07,940
Thank you very much, Alasdair.
506
00:25:08,060 --> 00:25:11,180
Carrie Anne:
Our next guest is well on her
way to creating the next
507
00:25:11,180 --> 00:25:14,900
generation of conservation
Technologists Natalie Shersby is
508
00:25:14,900 --> 00:25:18,080
a prolific computer club
volunteer running code clubs in
509
00:25:18,080 --> 00:25:20,930
schools for the past three
years and a CoderDojo in a
510
00:25:20,930 --> 00:25:24,140
community library. She has been
experimenting with household
511
00:25:24,140 --> 00:25:27,260
items and affordable
electronics components to make a
512
00:25:27,260 --> 00:25:31,940
motion sensitive Raspberry Pi
powered DIY wildlife camera with
513
00:25:31,940 --> 00:25:35,090
her CoderDojo an activity,
which she shares in issue
514
00:25:35,090 --> 00:25:36,170
sixteen of Hello World.
515
00:25:36,470 --> 00:25:37,550
Welcome, Natalie.
516
00:25:37,760 --> 00:25:41,300
What made you want to bring the
outside into your computing
517
00:25:41,300 --> 00:25:41,570
club?Hi
518
00:25:42,080 --> 00:25:44,720
Natalie Shersby:
Hi Carrie Anne. Thank you very
much for that lovely
519
00:25:44,720 --> 00:25:49,820
introduction there. So really,
we were about, I'd say, sort of
520
00:25:49,820 --> 00:25:53,720
six months into our CoderDojo
and the kids were all getting
521
00:25:53,720 --> 00:25:57,530
into like a good swing with
things and but it was coming up
522
00:25:57,530 --> 00:26:01,160
to that sort of the six week
break in the summer holidays and
523
00:26:01,220 --> 00:26:04,520
I thought, I don't really want
to break this off because
524
00:26:04,520 --> 00:26:05,570
they're all getting into a good
swing.
525
00:26:06,800 --> 00:26:11,060
And I know with my daughter, if
if she's like she's got her
526
00:26:11,060 --> 00:26:14,180
gymnastics and things, I know
she doesn't really like it over
527
00:26:14,180 --> 00:26:17,960
the summer because it stops and
I'm thinking right I'm going to
528
00:26:17,960 --> 00:26:19,520
need to put something on in the
summer.
529
00:26:19,880 --> 00:26:21,530
I want to put something on in
the summer.
530
00:26:21,980 --> 00:26:26,990
So I kind of thought, well,
what can what can we do that is
531
00:26:26,990 --> 00:26:28,520
sort of summer oriented.
532
00:26:28,790 --> 00:26:33,590
So we'd already made a wildlife
camera, my daughter and I, and
533
00:26:33,740 --> 00:26:35,930
it was brilliant. She was
really engaged.
534
00:26:36,050 --> 00:26:39,140
The photos that we collected,
we were there for hours looking
535
00:26:39,140 --> 00:26:42,560
at all these photos. And she
just she found it fantastic.
536
00:26:42,560 --> 00:26:43,940
She loved building the camera.
537
00:26:44,390 --> 00:26:46,700
So I thought that was a really
good one.
538
00:26:46,820 --> 00:26:50,780
That's going to transfer really
well to our CoderDojos.
539
00:26:51,380 --> 00:26:56,900
So we so we got all the kit
together and and we advertised
540
00:26:56,900 --> 00:27:01,370
the sessions and there were so
many kids wanting to come along
541
00:27:01,370 --> 00:27:04,430
and have the go. So we did it
over and over a series of about
542
00:27:04,430 --> 00:27:07,100
three or four weeks in the
summer time.
543
00:27:07,160 --> 00:27:09,920
So it was a small group to
start with.
544
00:27:10,130 --> 00:27:12,760
And then they've come, they've
built the camera, the take it
545
00:27:12,770 --> 00:27:14,210
home for a week that come back.
546
00:27:14,420 --> 00:27:16,940
They share with us what they've
captured, et cetera.
547
00:27:17,720 --> 00:27:18,890
And it was just amazing.
548
00:27:19,190 --> 00:27:21,890
James Robinson:
Did this interest come from from
you have you got a personal kind
549
00:27:21,890 --> 00:27:25,100
of interest in conservation and
wildlife or was just kind of
550
00:27:25,160 --> 00:27:27,590
spin out of this activity you
did with your daughter, where
551
00:27:27,590 --> 00:27:29,900
did that inspiration kind of
come from?
552
00:27:29,900 --> 00:27:32,780
Natalie Shersby:
So it was mainly my daughter
loves animals.
553
00:27:33,080 --> 00:27:36,050
We're, we sort of we go to the
Yorkshire wildlife park all the
554
00:27:36,050 --> 00:27:37,340
time, she loves it there.
555
00:27:37,670 --> 00:27:40,760
And so we're just we're sort of
trying to think of things that
556
00:27:40,760 --> 00:27:44,600
we could both do together to
get her a bit more into the
557
00:27:44,600 --> 00:27:49,400
technology stuff, because she's
she's sort of more arty minded,
558
00:27:49,400 --> 00:27:51,380
creative. But I thought, well,
what can we do?
559
00:27:51,380 --> 00:27:53,900
That's a little bit more
creative as well as the
560
00:27:53,900 --> 00:27:54,920
technology side.
561
00:27:55,460 --> 00:27:59,240
Having had that sort of the
feedback from her and her
562
00:27:59,240 --> 00:28:02,390
enjoyment of doing the project,
I thought it's going to
563
00:28:02,980 --> 00:28:06,320
translate very well to the
children at the CoderDojo.
564
00:28:06,530 --> 00:28:09,680
James Robinson:
You had a sort of a nice guinea
pig to help you out with the
565
00:28:09,680 --> 00:28:12,200
activity first. Iron out all,
the sort of technical
566
00:28:12,200 --> 00:28:15,860
challenges. And then how about
the students or the the children
567
00:28:15,860 --> 00:28:16,010
that were attending the
CoderDojo?
568
00:28:17,690 --> 00:28:23,750
Natalie Shersby:
So they ranged from sort of
around seven to about 14, mainly
569
00:28:23,750 --> 00:28:26,450
sort of primary school age, but
there were a couple that were a
570
00:28:26,450 --> 00:28:28,250
little bit older than that at
secondary school.
571
00:28:28,340 --> 00:28:30,830
James Robinson:
And which bits of the project
were they doing?
572
00:28:30,900 --> 00:28:32,360
So you mentioned that they
built the camera.
573
00:28:32,570 --> 00:28:34,540
Was there some programming
involved for them?
574
00:28:34,610 --> 00:28:36,890
I'm just interested in that
kind of their journey through
575
00:28:36,890 --> 00:28:37,470
the project.
576
00:28:37,470 --> 00:28:41,370
Natalie Shersby:
So all the components were sort
of laid out in front of them.
577
00:28:41,630 --> 00:28:42,670
They had a little kit.
578
00:28:43,190 --> 00:28:45,650
And so we went first of all, we
were sort of like we went
579
00:28:45,650 --> 00:28:49,360
through all the different bits
and pieces were it was really
580
00:28:49,630 --> 00:28:52,070
for a lot of them. It was the
first time they'd ever really
581
00:28:52,070 --> 00:28:54,530
seen the component parts for
different things.
582
00:28:54,770 --> 00:28:57,380
We used Raspberry Pi Zero
boards.
583
00:28:57,380 --> 00:29:00,740
So we showed them just how small
it was and how powerful it could
584
00:29:00,740 --> 00:29:03,980
be. We all together, we all
sort of did it in a big group.
585
00:29:03,990 --> 00:29:08,180
We all did each stage together
so that if anyone got stuck or
586
00:29:08,180 --> 00:29:09,440
anything, it was fine.
587
00:29:09,440 --> 00:29:10,460
Everyone was together doing it.
588
00:29:11,540 --> 00:29:16,130
And so we built the machine
together and got all the parts.
589
00:29:16,310 --> 00:29:20,180
I'd already pre drilled the
holes in the little plastic box
590
00:29:20,180 --> 00:29:22,460
for them and all that sort of
stuff.
591
00:29:23,300 --> 00:29:28,220
And then it was a case of we
just we used some software.
592
00:29:28,340 --> 00:29:30,860
We actually use the my nature
watch camera kits.
593
00:29:31,310 --> 00:29:34,220
And so the software was already
there for us to download.
594
00:29:34,580 --> 00:29:37,970
So we'd already downloaded that
and I showed them how to put it
595
00:29:37,970 --> 00:29:39,290
onto the SD card.
596
00:29:39,650 --> 00:29:42,710
So they did all that then and
then we just sort of loaded it
597
00:29:42,710 --> 00:29:46,590
off and everything. And the way
they went so first, they were
598
00:29:47,030 --> 00:29:53,870
trying to capture each other on
the camera and there and then so
599
00:29:53,870 --> 00:29:58,250
then they could take it home
and have it for about a week and
600
00:29:58,250 --> 00:30:01,430
they put it in their own
gardens and and then they came
601
00:30:01,430 --> 00:30:04,610
back and they showed just what
like what they were able to
602
00:30:04,610 --> 00:30:05,150
capture.
603
00:30:05,150 --> 00:30:08,300
Carrie Anne:
What kind of animals or what
kind of creatures were they able
604
00:30:08,300 --> 00:30:09,680
to capture in that time?
605
00:30:10,010 --> 00:30:14,870
Natalie Shersby:
There was mainly birds, cats,
squirrels, and somebody got a
606
00:30:14,870 --> 00:30:19,340
mouse, a little field mouse and
their own pets, their own, their
607
00:30:19,340 --> 00:30:23,000
own cats. We had a couple of
ducks, so.
608
00:30:23,000 --> 00:30:23,420
Yeah.
609
00:30:23,900 --> 00:30:27,440
James Robinson:
And what kind of impact did you
see on on the on the people
610
00:30:27,440 --> 00:30:29,210
themselves? How do they find
the project?
611
00:30:29,570 --> 00:30:33,420
Were they coming back keen to
kind of show what they captured,
612
00:30:33,440 --> 00:30:35,630
like what was the kind of the
impact on the on the on the
613
00:30:35,660 --> 00:30:35,880
kids.
614
00:30:36,260 --> 00:30:37,610
Natalie Shersby:
Yeah, they were they were really
engaged.
615
00:30:38,300 --> 00:30:41,630
And as I said before, it was
the first time some of them had
616
00:30:41,630 --> 00:30:44,300
really ever seen like component
parts and actually built
617
00:30:44,300 --> 00:30:49,070
something. So they got a real
special sense of achievement out
618
00:30:49,070 --> 00:30:51,810
of building that functioning
device and were able to get the
619
00:30:51,810 --> 00:30:56,270
use from that, that they were
so excited to show the group the
620
00:30:56,270 --> 00:31:00,470
different animals that they
captured. Some of them even took
621
00:31:00,470 --> 00:31:03,110
the cameras to like the
grandparents houses and my other
622
00:31:03,110 --> 00:31:08,030
friends houses and and sort of
snuck around and hid the cameras
623
00:31:08,030 --> 00:31:11,060
here, there, everywhere, and
like sort of what's going to be
624
00:31:11,060 --> 00:31:13,670
in your garden. I wonder if you
have something different to me.
625
00:31:13,910 --> 00:31:16,520
And and even the grandparents
got in on it.
626
00:31:16,520 --> 00:31:19,640
And a few weeks later I got
some messages...
627
00:31:19,860 --> 00:31:21,690
James Robinson:
They weren't they didn't count
as nature did they.
628
00:31:21,710 --> 00:31:22,140
They they weren't the subject
of footage.
629
00:31:25,460 --> 00:31:26,620
Natalie Shersby:
So even that.
630
00:31:26,630 --> 00:31:29,450
And the grandparents were like,
oh, this is this is really good.
631
00:31:29,450 --> 00:31:33,320
And I've got actually some
messages off the parents asking,
632
00:31:33,320 --> 00:31:36,080
well, where do we get these
things from we want to have a
633
00:31:36,080 --> 00:31:39,980
go. So it was like it seemed to
really appeal to people of all
634
00:31:39,980 --> 00:31:45,200
ages. And it was just it seemed
to us to sort of really extend
635
00:31:45,380 --> 00:31:47,930
extend out to the families and
the friends as well.
636
00:31:49,100 --> 00:31:54,200
But when they came back in and
were showing the things that
637
00:31:54,200 --> 00:31:59,180
they had found, we we had this
wonderful conversation in which
638
00:31:59,180 --> 00:32:01,770
we all discussed what other
sort of outdoor nature based
639
00:32:01,820 --> 00:32:04,580
projects that they might like
to build or to make or what they
640
00:32:04,580 --> 00:32:08,990
might like to do. And sort of
the suggestions from them ranged
641
00:32:08,990 --> 00:32:14,030
from automatic plant watering
devices to weather stations.
642
00:32:14,210 --> 00:32:19,220
And even one of them had their
big garden pond and wanted to
643
00:32:19,220 --> 00:32:21,860
make an automatic fish feeder
for that pond.
644
00:32:22,520 --> 00:32:25,670
So it really sort of got the
creative juices flowing and
645
00:32:25,670 --> 00:32:28,970
they're all like, oh, well,
what they'd like to do next, it
646
00:32:28,970 --> 00:32:30,200
was really, really, really
inspiring.
647
00:32:31,190 --> 00:32:36,350
James Robinson:
And what's interesting is the
real super like doable kind of
648
00:32:36,350 --> 00:32:37,690
projects right there.
649
00:32:37,730 --> 00:32:40,130
Often when you kind of pitch
things to kids and you say what
650
00:32:40,130 --> 00:32:42,530
do you want to make it, it's
like, well, I want to make a jet
651
00:32:42,530 --> 00:32:45,920
powered backpack you know, it's
often outlandish, but they're
652
00:32:45,980 --> 00:32:49,790
very, very practical, doable,
interesting projects.
653
00:32:50,160 --> 00:32:54,260
Carrie Anne:
What tips do you have for
teachers or club leaders to help
654
00:32:54,260 --> 00:32:55,700
them get started with a project
like this?
655
00:32:55,700 --> 00:32:56,900
Natalie Shersby:
For tips?
656
00:32:56,900 --> 00:33:00,890
I would say it's important that
you have fun with it and you can
657
00:33:00,890 --> 00:33:04,670
always start small, gain a bit
of confidence and work up to
658
00:33:04,760 --> 00:33:08,060
some bigger projects and
definitely include the learners
659
00:33:08,060 --> 00:33:10,250
in the discussions about what
they might be interested to
660
00:33:10,250 --> 00:33:13,490
build, because I feel that if
they're interested in the thing
661
00:33:13,490 --> 00:33:16,030
that they're going to make it's
all the better for.
662
00:33:16,810 --> 00:33:19,960
And I don't think necessarily
you'd have to start with a
663
00:33:19,960 --> 00:33:23,350
project like this. There are
lots of other activities that
664
00:33:23,350 --> 00:33:27,370
that could do to get out there
and to reconnect with nature.
665
00:33:27,640 --> 00:33:30,460
One of the things being, I
think Alasdair touched on it a
666
00:33:30,460 --> 00:33:34,050
bit earlier about the machine
learning aspects.
667
00:33:34,090 --> 00:33:38,000
There are these these like
plant different plant identifier
668
00:33:38,050 --> 00:33:41,440
apps now that you can get so,
you know, even if they're just
669
00:33:41,440 --> 00:33:45,100
taking a group of young people
out outdoors with them, with a
670
00:33:45,100 --> 00:33:48,730
tablet, with the plant
identifier app it and just get
671
00:33:48,730 --> 00:33:52,630
them to snap something that
they think looks good or is an
672
00:33:52,630 --> 00:33:56,080
interesting plant, and then
they can find out what it is and
673
00:33:56,080 --> 00:33:59,950
just and then even just grab
all of data and get it into some
674
00:33:59,950 --> 00:34:02,010
spreadsheets or stuff, stuff
like that.
675
00:34:02,060 --> 00:34:03,940
This is all a good learning
experience.
676
00:34:05,390 --> 00:34:07,760
Carrie Anne:
Yeah and they could take
photographs as well as the the
677
00:34:07,840 --> 00:34:12,400
plants outside and they could
train a model using Google
678
00:34:12,820 --> 00:34:16,000
teachable machine is the one
you can use images on that and
679
00:34:16,000 --> 00:34:18,520
they can actually train it to
identify the plants.
680
00:34:18,520 --> 00:34:20,710
They could build their own
version of that app right, which
681
00:34:20,710 --> 00:34:21,880
is super inspiring.
682
00:34:22,760 --> 00:34:26,330
James Robinson:
I think there's some Scouts
based activities that have a
683
00:34:26,330 --> 00:34:29,570
similar kind of feel where you
are going out and you're taking
684
00:34:30,140 --> 00:34:31,670
there on the Raspberry Pi
website, you can go out and
685
00:34:31,670 --> 00:34:33,970
you're looking to identify
plant leaves and so on.
686
00:34:33,980 --> 00:34:37,850
And it kind of takes a fairly
algorithmic approach, but it's
687
00:34:37,850 --> 00:34:40,970
kind of exposing some computing
kind of principles that are sort
688
00:34:40,970 --> 00:34:43,610
of going on there is we
identify and classify things
689
00:34:43,880 --> 00:34:46,100
says lots of yeah, it's great,
great to have lots of great
690
00:34:46,100 --> 00:34:47,930
activities that don't
necessarily require technology.
691
00:34:49,000 --> 00:34:52,240
Carrie Anne:
And we asked our listeners the
same question, have you ever
692
00:34:52,240 --> 00:34:56,580
conducted outdoor nature
technology projects with your
693
00:34:56,590 --> 00:34:57,780
classroom or with your cubs?
694
00:34:58,120 --> 00:34:59,440
And what did you learn about
them?
695
00:35:00,370 --> 00:35:04,740
James Robinson:
Erm I really like this comment
from Shashi Krishna, who talked
696
00:35:04,750 --> 00:35:07,240
about a project that they did
with some older students for
697
00:35:07,240 --> 00:35:11,440
Earth Week. They went away and
they collected some data sets on
698
00:35:11,440 --> 00:35:14,440
CO2 emissions and they
discussed endangered wildlife
699
00:35:14,440 --> 00:35:15,460
from local regions.
700
00:35:15,640 --> 00:35:18,570
And this was done with some
International baccalaureate
701
00:35:18,570 --> 00:35:21,430
computing students. They built
some interactive data dashboards
702
00:35:21,430 --> 00:35:24,850
and used them to sort of make
analysis and findings.
703
00:35:25,030 --> 00:35:28,300
And his reflection was that
cleaning up the data was the
704
00:35:28,300 --> 00:35:29,740
hardest part for the kids.
705
00:35:30,670 --> 00:35:33,940
And the next time they want to
investigate how students can get
706
00:35:33,940 --> 00:35:37,000
their own data for their
countries and local regions.
707
00:35:37,510 --> 00:35:42,490
Carrie Anne:
And Alan O'Donohoe, our insider
guide teacher from Hello World,
708
00:35:42,490 --> 00:35:45,520
said that with year 8 classes,
they created and programmed
709
00:35:45,520 --> 00:35:48,100
virtual fish that swam in the
tank using scratch.
710
00:35:48,250 --> 00:35:51,040
And if you fed the fish
correctly, created the right
711
00:35:51,040 --> 00:35:52,330
environment for them.
712
00:35:52,510 --> 00:35:56,330
Then they swam around in random
ways and displayed patterns that
713
00:35:56,350 --> 00:35:58,840
if you fed them too much, they
didn't swim as well.
714
00:35:59,080 --> 00:36:02,890
James Robinson:
I like this this idea for a very
maker-y kind of project from
715
00:36:03,310 --> 00:36:07,690
Jackie Tan, who this is a
project they've got planned for
716
00:36:07,690 --> 00:36:10,300
next year they're going to sew
a plant pot out of landscape
717
00:36:10,300 --> 00:36:13,660
fabric, put a yoghurt pot of
seeds in, make an auto watering
718
00:36:13,660 --> 00:36:15,970
machine and monitor the
progress of the plant over a
719
00:36:15,970 --> 00:36:18,610
period of time. I guess a very
nice kind of combination of
720
00:36:18,610 --> 00:36:21,400
different skills there, all
kind of themed around nature.
721
00:36:21,410 --> 00:36:25,000
It's a great project.
If you have a question for us or
a comment about our discussion
722
00:36:25,000 --> 00:36:26,740
today, then you can email via
podcast@helloworld.cc.
723
00:36:28,990 --> 00:36:30,620
Or you can tweet us at
HelloWorld_Edu.
724
00:36:32,950 --> 00:36:35,200
Carrie Anne:
My thanks to Natalie and
Alasdair for sharing their
725
00:36:35,200 --> 00:36:36,430
expertise with us today.
726
00:36:36,640 --> 00:36:38,320
Really inspiring stuff.
727
00:36:38,500 --> 00:36:40,840
You can read Alasdair's
article, Reconnecting with
728
00:36:40,840 --> 00:36:43,690
Nature in Your Classroom and
Natalie's Discovering Wildlife
729
00:36:43,690 --> 00:36:46,750
with my nature watch activities
in issue 15 of Hello World
730
00:36:46,750 --> 00:36:49,210
magazine. So what did we learn,
James?
731
00:36:50,530 --> 00:36:55,420
James Robinson:
Well, apart from Penguinologist
as my new future sort of backup
732
00:36:55,420 --> 00:36:58,480
career, potentially, I thought
it's really interesting to hear
733
00:36:58,690 --> 00:37:01,660
about all the different kind of
ways that we can engage in
734
00:37:01,660 --> 00:37:03,790
nature, the importance of it
for our pupils.
735
00:37:04,660 --> 00:37:05,860
I found that really
fascinating. How about you?
736
00:37:06,460 --> 00:37:09,460
Carrie Anne:
Well, I'm just really glad that
two of my favourite things at
737
00:37:09,460 --> 00:37:12,160
the moment. So one is kind of
nature in my garden and the
738
00:37:12,160 --> 00:37:14,760
other one is machine learning,
which I'm super interested in
739
00:37:15,460 --> 00:37:18,940
at the moment can be combined
together in such a great way.
740
00:37:19,060 --> 00:37:21,340
I'm going to get on that
hedgehog thing like right now.