Rethinking Education: Is Connection the New Content?
The traditional classroom model, built on the transmission of content from expert to learner, is facing a profound challenge. What if the heart of learning isn't content at all—but connection? This question has fueled a quiet revolution in educational technology, one that emphasizes distributed networks over centralized control.
We recently had the opportunity to trace this revolution's origins with Stephen Downes, a philosopher-turned-edtech pioneer with the National Research Council of Canada. Downes offers a powerful blueprint for reimagining education in an information-rich world, an approach he co-developed that emphasizes genuine interests, real work, and the tools that serve judgment rather than replace it.
From Philosophy to the First MOOC: The Birth of Connectivism
Downes, alongside collaborator George Siemens, didn't just question the content-centric model; they proposed an entirely new theory for a digital age: Connectivism.
What is Connectivism?
Connectivism posits that knowledge exists in the connections between different "nodes" or entities—people, organizations, libraries, websites, and information sources. Learning, in this view, is the process of creating, navigating, and growing these connections. It’s a learning theory uniquely suited for a world where information is abundant and constantly changing.
This theory wasn't just academic; it sparked a practical experiment that would change the landscape of online education: the first-ever Massive Open Online Course (MOOC).
The "Bar Napkin" Moment and Distributed Power
The genesis of the MOOC came from a moment of casual collaboration—the now-famous "Memphis bar napkin moment." The result was CCK08 (Connectivism and Connective Knowledge, 2008). What made this truly massive and open wasn't its content, but its simple design choice to distribute power:
- Decentralized Architecture: Unlike traditional courses hosted on a single Learning Management System (LMS), CCK08 allowed participants to use their own blogs, wikis, and social media platforms.
- Ideas Flow Across Many Nodes: The "course" acted as a hub for interaction, but the real learning—the creating, connecting, and discussing—happened in the learners' personal spaces. This distributed approach was the key to scaling the course to thousands of participants without the platform crashing or the instructor burning out.
The Network Model: What Makes a MOOC Actually Work
According to Downes, a truly effective MOOC, or any modern learning experience, must behave like a network, not a classroom. This means prioritizing federated, open architectures over centralized, proprietary platforms.
Course as Catalyst, Not Warehouse
Downes redefines the purpose of a course:
- Time-Boxed Catalyst: A "course" should not be a static content warehouse, but a time-bound, focused eventdesigned to introduce ideas, foster connections, and spur creativity. The learning happens after the course ends, as participants continue to engage with their newly formed network.
- Voluntary Participation: In a connectivist environment, participation is voluntary. This dramatically reduces privacy risks and, more importantly, increases learner agency. Learners who freely choose to participate are more engaged and invested in their own learning paths.
Reframing Control: The Content MacGuffin
Schools often grapple with the tension between control, content standards, and surveillance. Downes offers a crucial reframe: Content is the MacGuffin—the necessary but ultimately unimportant plot device.
In a world where information is instantly accessible, the true learning is not in consumption but in:
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Chris (00:13):
Welcome back everyone to another episode of Shifted
Podcast.
Um our school year is uh stillkicking.
Uh we're just getting into uhour September, October.
Well, still mid-September,getting into October.
Probably by the time you hearthis, it will be October.
Um and I have a great guest,Stephen Downs, coming in, um,
(00:36):
who works for the NationalResearch Council of Canada with
a specialization in digitaltech, which you know that's our
jive here at Shift Podcast.
We love all this stuff.
And Steven's such a thinker, aphilosopher, and um just a great
mind to kind of tap into abouteducation, the digital landscape
(00:56):
and uh connectedness.
One of his big things isconnectivity.
That how do we stay connectedthrough the X's and O's of
digital technology?
Steven, thanks so much forjoining me today.
Stephen (01:10):
Thanks.
Pleasure to be here.
Chris (01:13):
So, Steven, can we uh rewind time a little bit?
And could you kind of tell us alittle bit about how you kind
of came to what you're doingtoday?
Um, what were some of theexperiences that brought you to
the job you do today um at thecouncil?
Stephen (01:31):
Um well pretty much happenstance uh I was studying
philosophy in university andfell into a job teaching
philosophy by distance educationwith Athabasca University.
Speaker 01 (01:47):
Okay.
Stephen (01:48):
I did that for seven years with them and um always
had an interest in computers.
Uh I was actually enrolled in acomputer certificate course
even before my philosophydegree.
And um so it was a natural totry to adapt distance education
to computers.
I created my own bulletin boardservice way back when.
(02:12):
So um, while I was withAthabasca, I got hired by uh a
Cineboy and Community College todevelop their online presence,
uh, everything from theirwebsite to their distance
learning.
Uh and I built uh a learningmanagement system while I was
with them, and that's basicallywhere it took off from there.
(02:34):
I went to the University ofAlberta and then in 2001 came to
the NRC.
Chris (02:40):
Awesome, awesome.
Great experiences, and I lovehappenstances.
I mean, those are sometimes weget to where we're at just by
like a conversation or replyingto an email.
Who knows, right?
It's just so uh amazing howthat happens.
I kind of want to drill down alittle bit on the MOOCs.
Now, MOOCs are online learningenvironments for whoever, right?
(03:04):
I mean, you can have them in aplethora of different varieties.
It could be on philosophy, itcould be on you know car
mechanics, whatever you want,right?
You can probably find oneonline.
How did you get started withthat?
How did you I know that youstarted with George uh Siemens,
right?
Siemens?
Stephen (03:21):
George Siemens, yeah.
Um, so I've always been onabout networks, and that goes
back to my days studyingphilosophy.
And back then I I representeduh knowledge as being based in
similarity and association, andthat led me to something called
(03:43):
connectionism, which was aphilosophy or approach in
computer science, uh, which wasbased on artificial neural
networks, which totally meshedwith what I was thinking at the
time.
And so uh, as I developed myown thinking about online
learning and onlinecommunication generally,
(04:06):
networks fell naturally intoplace because, of course, we
were on the internet works,right?
Speaker 00 (04:12):
Right, right.
Stephen (04:13):
Um so George Siemens came along with an article
titled Connectivism, where hedrew out some uh features of
networks and applied them tolearning, uh, which totally
meshed with what I was thinking.
And um, so consequently, Georgeand I got together.
(04:35):
He held a conference calledConnectivism.
It was an online conferencehugely popular.
So we're sitting in a bar oneday in Memphis, believe it or
not, and we decided to offer anonline course about
connectivism.
Um and we decided that thecourse shouldn't just try to
(04:58):
explain connectivism, it shouldtry to model connectivism.
Um, and that was a good thingbecause we had 2200 people show
up.
And because we designed thecourse as a network and not one
of your standard, you know, uhpeople sign up for a discussion
(05:18):
list or whatever, the courseworked.
We could accommodate that manypeople, and that prompted Dave
Cormier to call it a massiveopen online course, and thus the
MOOC was born.
Chris (05:31):
Wow, wow.
And there are many forms, as Ialluded to before.
What are effective MOOCs forthese online?
Which ones do you findpersonally grab not only you
know the content that you wantto, you know, eat, consume, but
also have that personalconnective um aspect to them?
(05:55):
What are some of the componentsthat you would look for in a
MOOC that would allow thatconnectivity to happen?
Uh these days, um, and you haveto keep in mind, right?
We're we're almost 20 yearsfrom the first MOOC.
That was in 2008.
So these days, I would say itneeds to be in some way
(06:20):
federated.
Back then, I would have saiddistributed, and I still mean
the same thing when I use thetwo different terms.
And what I mean by that is itshould not be based in a single
place.
Um, and just as an aside,that's what the commercial MOOCs
all did is they were one bigwebsite with a whole bunch of
(06:44):
Amazon uh AWS backing forstorage and stuff and AI to do
the marking and that, but theywere all just centralized, and
none of those models survived.
One by one, they all ended upselling out one way or another
and becoming closed products.
(07:05):
But a massive open onlinecourse really ought to be a
network, of course, right?
Not one site, but manyconnected sites.
And the properties of a MOOCfollow from that thinking,
right?
Massive because networks cansupport mass much better than
(07:28):
single centralized services.
Open because it's not a networkif it's closed, right?
Open means it can join andleave, that content can come in
and come out and flow freelythroughout, right?
Online, because well, that'sthe easiest place for us to have
networks.
And the only thing that'sdifferent about a MOOC, um and
(07:53):
just uh a network generally isin the word course, and here
course implies kind of twothings.
First of all, a fixed startdate and fixed end date, so it's
it's a moment in time, andsecond, some kind of well, I
(08:18):
used to talk about going back tothe original Oxford-Cambridge
model where the learning was infact all organized by the
students who would gather aroundthe professors, and each
student would have a professoras a mentor.
But what they would do is theywould convince, and they had
(08:39):
that this they actually had todo this, they had to convince a
professor to offer a course oflectures, where course in this
case means series of lectures.
And so that that would form thecore of the MOOC, right?
There'd be a course ofdiscussions on a sim on a single
(09:01):
topic, uh, you know, hosted byone of us and bringing in
people, and then the wholenetwork would surround that and
discuss that, which is what thestudents back in the
Oxford-Cambridge model did.
So it's taking that model andapplying it to the digital age.
That's interesting.
(09:21):
Wow.
And do you feel that when we'relooking at these, like how the
technologies, the ethics, theprivacy of all of this, like,
because you do say it's open,right?
It's an open um network, youcan come and go.
Like more and more we'reconcerned at schools anyway, and
(09:42):
like school boards, and youknow, they're worried about the
ethical use and the privacy use.
Like, how does how would thatcontour or cause a problem with
it being used more prolificallythroughout, example, our youth
sector education, like for highschool students, example?
Stephen (10:02):
Well, yeah, I mean, it depends on what your view of
education is.
Um I'm just trying to thinkabout how to word this
diplomatically, because uh, youknow, a lot a lot of people are
very concerned about controllingeducation, controlling the
content of education, um, andthey're very concerned about
(10:25):
education imparting specificknowledge and specific content
and even more specific values,especially cultural values,
right, on students.
Um and that's not really themodel that a MOOC follows.
Um a MOOC is much more aboutthe students themselves
(10:49):
determining for themselves wherethey want their learning to go.
Um, George and I used to say,and I still say, you know, the
content, quote unquote, is aMacGuffin.
Right?
Uh it's the thing that we'retalking about, but it's not the
thing that we expect people tolearn.
(11:11):
Uh you know, it doesn't matterif they learn the content.
The content is just thestarting point.
It's the seed, it's thecatalyst, it's the thing that
gets people going.
Um, and what they're actuallydoing in our course and in any
course is exercising their mind,exercising themselves, thinking
(11:35):
about these topics, workingthrough these topics, hopefully,
and we really encouraged this,creating and writing about these
topics.
Right?
Um so it didn't matter whetherthey learned the topic, what
they what they were learningwere the skills needed to deal
(11:57):
with kind of things like this.
So uh you know, it it's adifferent way of thinking about
learning.
Um coming into a massive onlinecourse, you know, we we talk
(12:17):
about things like privacy andthings like that.
Um my courses, um, I don't evenask for registration.
I don't want to I mean I guessI'm kind of curious, but it
doesn't matter to me whether ornot I know who or how many
(12:39):
people have signed up.
Right?
I mean this this is you knowthe the the need for
registration isn't the same asthe need for learning.
There's incredible for you.
Um, you know, so uh if I wantedto sign up for a mailing list,
then I'd need their emailaddress, otherwise I wouldn't
(13:00):
know to send where to send thenewsletter, but they could just
as easily use the RSS feed andaccess to that as anonymous or
as anonymous as it gets, right?
Speaker 01 (13:11):
Right, right, right.
Stephen (13:12):
So there's much less concerns about privacy and
surveillance.
A person's contributions to aMOOC are are not required by the
curriculum, right?
They're voluntary.
Each person contributeswhatever they feel comfortable
contributing.
Speaker 01 (13:31):
Okay.
Stephen (13:32):
So if the person comes into the course having a basic
understanding of you know notsharing too much on the
internet, then you know there isa prerequisite there.
Um then from privacyconsiderations they should be
fine.
Um so you know these kind ofquestions don't arise nearly as
(13:58):
much in student, I don't want tosay student managed because
that's not quite the right word,right?
But and and and not evenstudent-led, perhaps
student-driven might be a betterword, but even that's not quite
the right word.
But you get the idea, right?
Uh these questions don't arisethe way they do in a course with
(14:21):
the prescribed curriculum,specific content that a student
must learn, specific activitiesthat a student must undertake,
specific rules of participationthat a student must follow,
right?
If you remove the word mustfrom learning, uh a lot of the
(14:42):
questions about privacy,security, et cetera, disappear.
Chris (14:45):
Interesting.
Interesting.
And I mean, one of the thingstoo that kind of struck me when
you're when you're speaking wasthat this need for content that
we have in structured schooling,right?
It's all driven by content.
We test it, uh, you know, wehave goals you have to set.
And we often forget about theperson behind all of that and
(15:08):
those skills that we need todevelop in school and not just
content.
I mean, I think about school, Imean, uh what I remember is my
personal interactions and playsand sports I did.
I don't really remember whathappened in C4Math at all.
Stephen (15:27):
Well, we don't know how to add, but that's basic
numerous, you know.
Chris (15:32):
I mean, they say grade eight is probably about as much
as you need to survive in thisworld.
Where do where does where do weconnect those two from the
content to the person?
And also help in thatdevelopment not only of the
knowledge of this of whoever isinteracting with the MOOC, but
also how do we develop thempersonally and their skills and
(15:55):
their critical thinking, theiryou know, creativity, etc.
Yeah, I mean, I know those areimportant questions.
Uh the last thing we want uh isto have a generation of
students who are incapable offunctioning, especially in a
(16:18):
modern industrial informationage economy.
Um, we we can look at anycountry where the education
system has collapsed or beencompromised to see the results
of that.
Um we we don't want that tohappen, obviously.
(16:38):
Um since the very beginning ofmy work in educational, I've
urged caution.
And I still urge caution,right?
You don't jump into somethingwhole hog before you know what's
gonna happen out the other end.
unknown (16:55):
Right.
Stephen (16:56):
Um which is unfortunately what I think we do
sometimes.
Uh I tend to have two thingsthat are are my my guiding
points here when I when I thinkabout this question.
Um the first guiding point isif a thing is fundamental, it's
(17:21):
gonna show up as soon as you tryto do something.
Right?
Think about that.
Yeah, uh think about anythingsimple, like we're having a
conversation, right?
The need for language is gonnacome out, right?
Um, you know, the better we arewith language, the better the
(17:43):
conversations we're gonna have.
And that that's going to betrue with a lot of things.
If if a if a person wants to, Idon't know, um sew dresses,
like pull that out of the air,right?
They're gonna have to learn toread.
They're gonna have to learn towork with patterns and
(18:05):
directions.
The more they get into it, themore these fundamental skills
will become important.
Measurement, mathematics, area,geometry, uh, 3D geometry if
they're doing fashion, right?
Uh you know, all of these willcome into play.
(18:26):
And and a whole bunch of othersubjects, right?
Chris (18:29):
But you know, so the fundamentals your your starting
point is the hook.
Where where are they gonna findentry into whatever you're
asking them to do?
Right, right.
Stephen (18:42):
The fundamentals will emerge, right?
Right.
So it's it's our role aseducators to make sure that the
resources are there in place forthem to learn these when they
need them, but you know, forcingthem on them, probably not the
best way.
And that's leads obviously, andyou've already alluded to it.
(19:04):
The second point is what is itthe person wants to do?
Right.
So a couple, there's there's acouple of caveats there, right?
Because you know, uh peoplesay, well, all this all the
students, all the kids want todo is play.
Well, strictly speaking, thatmight be true, but if you look
(19:27):
at what play amounts to, in manycases it amounts to imitating,
uh imitating what they seearound them.
Uh so you see them playing alot of sports.
Why?
Well, they see sports on TV,uh, you know, uh, or they see
the older kids playing sports orwhatever.
You know, so uh you kind ofneed an environment that's
(19:51):
inspiring that will give kidsideas what they want to do,
right?
Um and you need to provide therole models for them.
Um, and and and then the accesspoint into whatever it is they
want to do.
Some kids will want to playbasketball all day, every day.
(20:14):
Um should this be encouraged toa certain point, sure, why not?
Um, you know, they they will bedeveloping themselves as
athletes.
And you know, again, the betterat being an athlete, you're
going to need to learn a bunchof stuff, right?
(20:37):
It's not just gonna be aboutthe physical performance.
You're gonna want to getbetter, you're gonna want to
learn about arts and and andphysics, uh, and tactics, uh,
and physical fitness and all ofthat.
Right.
Is it is it okay for a personto focus on that in their life
(20:57):
for the rest of their life?
Sure, why not?
They don't need to be a workerin a factory these days.
Nobody needs to be a worker ina factory, uh, it's all
potentially automated.
So that that's really gonnatake, you know, and and uh so
other kids will want to makerobots.
I would have been one of thosekids, except my hands were not
(21:20):
very good.
But you know, maybe, maybe wellno, I played with Mechano and
things like that, and I was allso yeah, that that never really
worked, but but computers wouldhave been a natural for me, and
were a natural once I had accessto them.
Right, right.
And so but the thing is, right,to a large degree, kids can
(21:48):
follow their passions and itwill lead to a good outcome.
And then then we can deal withthe exceptions.
There is there will probably beexceptions, but if you think
about it, dealing with theexceptions and guiding them so
that you know they actually dodevelop worthwhile skills and
(22:10):
and abilities is better thanstamping the same quote unquote
foundational knowledge on all ofthem, in my opinion.
Chris (22:20):
Right, yeah, totally, totally.
Right, right.
But still kind of satisfyingthe role that they are they
their interest guiding them to,and then kind of pulling in all
(22:42):
this stuff as you're goingdiscovering, you have no choice,
right?
Yeah, that's reallyfascinating.
I love, I love there's somegreat, great quote quotes in
there, Steven, for sure.
Stephen (22:53):
And and just as yeah, this is not none of this is my
unique idea, right?
There's there's a lot of peoplewho have said this before me.
Um everyone from Frieri toIllic to uh uh well John Holt
would have mentioned it.
Um other names.
(23:16):
Um there's there's one personin specific, I can't remember
his name, but oh uh dang, it'sright on the took of my tongue.
I have Alvin Toffler, butthat's not it.
Chris (23:29):
Well, maybe it will by the end of the day.
It'll come back.
Boom!
Speaker 02 (23:34):
It'll come back.
Chris (23:35):
Steven in walks AI, right?
Now we know since November2022, things have dramatically
changed, not only in education,but the world is feeling these
waves coming through.
We know it's not goinganywhere.
How do you see AI supportingyou know online learning
(23:58):
connectivity?
Like, is it gonna be an assetto us?
And what do we have to get ourheads around or beyond?
Because I know a lot of thetimes right now in in education
is kind of seen as well, it's acheating machine, you know, it's
like a quick way to get arounddoing the work where there's no
critical thinking.
And where do you stand on AI?
(24:19):
Like, what what do you what areyour hopes and also kind of
your cautions about you knowgoing all in with AI in in in in
in learning?
Stephen (24:29):
Yeah.
So well, again, first of all,be cautious and conservative,
you know, you know, like don'tjust go all in on AI because you
don't know what you're goingall in on.
But that's it.
Okay, that's the first thing.
Second thing is AI is notmagic, it's math.
(24:50):
Okay, that's why it's not goingaway.
So if you know, you you cancriticize open AI and meta and
anthropic all you want, andthere's lots of reasons to
criticize those companies.
The fact itself of the math isnot going away because the math
(25:10):
is networks.
The networks is the stuff thatI've been talking about my
entire life, right?
Uh that's not going away.
AI is based on connections,it's based on strength of
connections between individualunits in a network.
Um, and in large languagemodels, it's based on strength
(25:32):
of connections between words orperhaps parts of words.
And in some of the more uhrecent AR systems, strength of
connections between words andphrases and longer sentences,
increased attention, if youwill.
So none of that's going away,none of that is magic.
(25:55):
Okay.
Uh it does point to um howirrelevant the content is.
Right.
Chris (26:05):
Um I think that's the big exposure too, right?
That yeah, I mean, we thoughtwhen when kids had handhelds in
Google, yeah, forget about it.
I could go and like ask theteacher a question now, and
they're suddenly the rulesreverse.
I think AI like trug liketriples that.
Stephen (26:24):
Yeah, yeah.
So now, interestingly, um,because you know, artificial
intelligence, properlyso-called, originated in a
research project to try toemulate human reason using
computers, right?
Hence artificial intelligence,right?
(26:46):
Um and it has been more or lesssuccessful.
The part that hasn't reallychanged is emulating humans, and
many of the human foibles arefound in artificial
intelligence.
Um, for example, confidentlyasserting something that is not
(27:09):
true as being true.
Humans do that all the time,right?
Sometimes on purpose, which wecall lying, sometimes by
accident, um, which we call, Idon't, I don't think we even
have a name for it.
Um, you know, um depending onhow they're set up, an AI system
(27:31):
might be a sycophant, mightmight say, oh yes, you're so
correct, and all of that.
So you you can't just acceptwhat an AI says as necessarily
true.
Um now that should have beenthe case all along, um, but
(27:55):
especially well, I was gonna sayespecially recently, but I
suppose it's something that'salways been the case through
human history.
Uh, we've been taught to acceptwhat the authority says as true
and objectively true, um,especially in the age of
science.
Um, but you know, even in theage of faith, I guess.
(28:18):
Um and of course, that shouldnever have been the case.
We should always have beencritically reflective of what
anyone tells us.
And uh, you know, the thescientific method, properly
so-called, basically is a set ofmechanisms that enables a
(28:40):
person to determine whether ornot something that is said to be
true is in fact true.
And it involves um, you know,trying it for yourself, getting
the same information frommultiple sources, seeing if it
stays true over time, um,imagining counterfactuals and
(29:03):
falsification and testing andyou know, those sorts of
principles, right?
And we can be really loose andfuzzy about what those
principles are, and they'llstill more or less work.
So there's a strong correlationbetween what we think of as
scientific method and what wethink of as critical thinking,
although, bracketed aside,there's a whole industry of
(29:27):
false critical thinking.
Um beginning with De Bono andcontinuing on.
Not to say that everything deBono says is false.
There are ways of broadeningyour creative and imaginative
(29:53):
powers, not determining whetheror not something somebody tells
you ought to be believed.
See the distinction, right?
Speaker 01 (30:01):
So yeah.
Chris (30:02):
Um, and so you have you need to be kind of careful about
that when we talk about whatwhat we mean by critical
thinking.
I've I've seen thatmisunderstanding play out.
unknown (30:13):
Okay.
Speaker 02 (30:14):
So, um, so how do you teach critical thinking?
Well, not as a subject.
Speaker 00 (30:21):
Right.
It's again kind of a livedexperience.
Like you get better at it bydoing it.
Speaker 02 (30:27):
You get better at it by doing it, and you do it,
especially when you're a kid,when you see other people doing
it.
Speaker 01 (30:37):
Right.
Stephen (30:37):
Right.
A big part, a big part of mywork over the years has been to
try to offer a model of what Ithink critical thinking is.
And I have my daily newsletter.
Yes.
And each article that I review,I give it a little bit of
(31:01):
critical thinking, right?
So you read my newsletter, yousee uh four, five, six, eight
items of me doing the criticalthinking thing on an article.
Um, you know, and in my longerwork, I try to do longer
instances of critical thinkingand develop the whole thing.
Um that's part of the answer.
(31:22):
The other part of the answer islet's think about how how do I
want to put this?
Um let's think about whatthinking is.
Let's think about what evencritical thinking is, not in
terms of a process or a method,but in terms of how we know
(31:46):
what's true.
No, no, I don't even want tosay that because truth is an
attitude we have toward aproposition.
It's what we call apropositional attitude.
Truth is a label that we giveto a sentence in a language, but
(32:06):
our our thinking doesn't workin languages, right?
Uh we hear languages when wethink, but that's our perception
of ourselves thinking.
What a mess.
Chris (32:17):
What a mess.
What we do is what the computersdo.
We recognize patterns.
Right.
Stephen (32:27):
All right.
So what we want to model ispattern recognition, pattern
testing, all of that.
Um there's a whole story Iwould tell.
And you know, if we we thinkabout the different types of uh
logic and critical thinking,these are different forms of
(32:48):
pattern recognition.
That's why Sesame Street got itright when they did this whole
one of these things does notbelong.
They're after patternrecognition.
Chris (33:01):
Great.
Could that be like that's apart of computational thinking,
right?
Like where you have certainpattern recognition, like
there's certain mathematicalconcepts that are more
philosophical in a sense, butthey're hemmed to you know, math
and science and like reality.
(33:22):
Um I I've always been reallyfascinated with that mindset
instead of a math thinking.
Like, I wish instead of math,you would have taught me more
about computational thinking, sothat I start to understand
patterns and connections betweenthings and sequencing, and
which which, like you said, isrunning not only our
(33:45):
technologies, but AI is um Imean it's a prediction machine
in in essence.
Stephen (33:51):
Well, yeah, uh absolutely so is the human
brain.
It's a prediction machine.
Uh well the but it's also likea super sensitive uh content
(34:11):
receiver, uh signal receiver,you know.
Like uh, you know, we have wehave the various senses in that,
but it's like our head is ourantenna, right?
It's well, our whole body isthe antenna, right?
So we're very sensitive to theenvironment and we and and
(34:34):
signals, causes, sensations,whatever, come in, and then
we're very sensitive to whatthey are, and then we look at
the patterns in thosesensations.
Um, and some of the patternsare sounds, and some of the
patterns are words, and so on.
Chris (34:53):
Um fascinating, Steven.
That's like you're making methink of things in different
ways here.
I love this.
Stephen (35:00):
And we go back to computational thinking.
Uh we think about whatcomputational thinking is, and
it's fascinating if you thinkabout it.
Uh, what is it?
Right.
So what what's a computerprogram?
Computer program is a set ofdeclarative statements, perhaps.
(35:21):
Uh, but really the the magic ina computer program works um in
the form of conditionalstatements.
Right.
Okay.
If this then that.
And and the rest of computerscience, um, as any logician
will tell you, is logicallyequivalent to a set of
conditional statements, right?
A loop is a type of conditionalstatement, right?
(35:45):
Uh a series is a type ofconditional statement, right?
If you've done this, then dothis.
If you've done this, then dothis.
What is a conditionalstatement?
Conditional statement is afascinating thing.
I spent a long time studying itin my youth from a
philosophical perspective.
My first published paper everwas in fact called Conditional
(36:10):
Variability.
Um and what makes a statementif X then Y true?
Right, and we think it's justyour standard truth table, but
there's so much around a wholecontext around that.
Ultimately, what a conditionalstatement is is a pattern
(36:32):
recognizer, right?
If something is the case,right?
So, but pattern recognizersdon't, and this is the this is
where neural networks were sobrilliant.
Pattern recognizers don't haveto recognize patterns exactly.
Speaker 01 (36:52):
Okay.
Chris (36:53):
Um and and you know is that some of the bit of like
with AI, like withhallucinations sometimes, like
they'll want to satisfy, but itmight not fit exactly to that,
but they'll work their wayaround it somehow.
Stephen (37:12):
Have you ever walked in a crowd and thought you
recognized someone and then itturned out it wasn't the person
you thought you recognized?
Chris (37:20):
Totally, totally, yeah.
So that's that's a good, goodconnect.
That's a great example, Steve.
Wow, yeah, totally.
Stephen (37:29):
That's exactly what's happening with a hallucination,
right?
Um, you and this is a reallyimportant point.
You're predisposed to recognizea certain set of things, right?
Right?
People in your family, uh,famous people, um, people who
(37:49):
look different, uh whatever,right?
I mean it's the set isdifferent for everyone, right?
Um and I'm pretty sensitive tothis because my eyes are so bad
that I'm really bad atrecognizing people, horrible at
it.
And I've insulted so manypeople over the years as a
(38:11):
result.
I'm just but you know, in theabsence of something, you feel
its presence keenly.
Um, but you can recognize on apartial pattern, right?
That is, in other words, youcan recognize things based on
similarity rather than identity.
But what makes similarity worksis this precondition, or as we
(38:38):
say these days, context.
Right?
So what is computationalthinking?
It is recognition bysimilarity, what counts as
similarity, context, and so nowyou're into topics, you know,
(38:59):
how do you do pattern matching?
Um, how do you doclassification, categorization,
but not in terms of necessaryand sufficient conditions, but
in terms of similarities orpartial representations.
What is a representation?
What counts as arepresentation?
(39:20):
If I told you this is uh KarimAbdul Jabbar, first of all,
let's check.
Steven's holding up a he'sholding up a spoon.
Chris (39:29):
Yeah, you recognize the name, right?
Speaker 00 (39:32):
Yeah.
Speaker 02 (39:32):
Okay, good.
Just checking.
Chris (39:34):
Totally, totally.
I'm just because this is justgonna be audio.
And so Steven was holding up aspoon and asked me that
question.
Stephen (39:43):
And now if I do this, yeah, I just conveyed a signal
to you that you can probablyinterpret.
And again, nobody on the audiosaw that.
I bounced the spoon a bit andmade it leap up toward a basket.
Speaker 01 (40:02):
Right.
Stephen (40:03):
There was no actual basket, but you would have
inferred that there was a basketthere.
Now, you know, and I know therewasn't really a basket there,
but that was never the issue.
The issue was did you correctlyinfer that I intended you to
infer that there was a basketthere?
(40:23):
That's computational thinking.
So it's not about followingrules and principles and
constructing algorithms.
It's about pattern matching,data awareness, um,
classification, categorization,representation, those kinds of
(40:46):
topics.
And those are the kinds ofskills that are fundamental in
an information age, not content.
Chris (41:01):
Right, right.
Where we are now, which is um II I love I love your words,
Stephen.
And I mean, the reflectionsthat you're you're making me and
I know the listeners have isjust out of this world.
I really, really appreciate thetime.
I know that we're running a bitlong, but I just this is so
(41:23):
fascinating to me.
I mean, we could just keepgoing and going about this, but
I do want to respect our time.
I'm gonna have you back,Steven.
It would be great to just havea follow-up on this because it's
so um, I love how philosophyideas connect with tech.
I mean, I love that marriage orthat that relationship that
(41:45):
they have.
Um, so I'd love to dive deeperinto that.
If you want more of Steven, hedoes, as he alluded to, OL
Daily.
It's a great newsletter.
You can find it on LinkedIn oron his site, which I'll put in
the descriptor, um, so that youhave access to all these great
um ebooks he has, and also, likehe said, his reflections, his
(42:05):
critical thinking on articles,etc.
And he's he's prolific ingetting these out, and they're
really good.
So I recommend those to uh youlisteners as well.
Steven, this has been anabsolute pleasure.
Uh it's been such um I I I needto sit and just think a little
bit with everything that we'veexchanged today.
(42:26):
Um, but I would really love touh continue this one day.
Stephen (42:30):
Sure, it'd be my pleasure.
Happy to do it.
Chris (42:32):
Amazing.
Well, Steven, you have yourselfa great day.
And again, thanks for joiningus and and sharing some of your
knowledge with us.
I think we're all that muchsmarter today, now after this.
Stephen (42:42):
So thank you.
Oh, you're very welcome.
Welcome back everyone to another episode of Shifted
Podcast.
Um our school year is uh stillkicking.
Uh we're just getting into uhour September, October.
Well, still mid-September,getting into October.
Probably by the time you hearthis, it will be October.
Um and I have a great guest,Stephen Downs, coming in, um,
(00:36):
who works for the NationalResearch Council of Canada with
a specialization in digitaltech, which you know that's our
jive here at Shift Podcast.
We love all this stuff.
And Steven's such a thinker, aphilosopher, and um just a great
mind to kind of tap into abouteducation, the digital landscape
(00:56):
and uh connectedness.
One of his big things isconnectivity.
That how do we stay connectedthrough the X's and O's of
digital technology?
Steven, thanks so much forjoining me today.
Stephen (01:10):
Thanks.
Pleasure to be here.
Chris (01:13):
So, Steven, can we uh rewind time a little bit?
And could you kind of tell us alittle bit about how you kind
of came to what you're doingtoday?
Um, what were some of theexperiences that brought you to
the job you do today um at thecouncil?
Stephen (01:31):
Um well pretty much happenstance uh I was studying
philosophy in university andfell into a job teaching
philosophy by distance educationwith Athabasca University.
Speaker 01 (01:47):
Okay.
Stephen (01:48):
I did that for seven years with them and um always
had an interest in computers.
Uh I was actually enrolled in acomputer certificate course
even before my philosophydegree.
And um so it was a natural totry to adapt distance education
to computers.
I created my own bulletin boardservice way back when.
(02:12):
So um, while I was withAthabasca, I got hired by uh a
Cineboy and Community College todevelop their online presence,
uh, everything from theirwebsite to their distance
learning.
Uh and I built uh a learningmanagement system while I was
with them, and that's basicallywhere it took off from there.
(02:34):
I went to the University ofAlberta and then in 2001 came to
the NRC.
Chris (02:40):
Awesome, awesome.
Great experiences, and I lovehappenstances.
I mean, those are sometimes weget to where we're at just by
like a conversation or replyingto an email.
Who knows, right?
It's just so uh amazing howthat happens.
I kind of want to drill down alittle bit on the MOOCs.
Now, MOOCs are online learningenvironments for whoever, right?
(03:04):
I mean, you can have them in aplethora of different varieties.
It could be on philosophy, itcould be on you know car
mechanics, whatever you want,right?
You can probably find oneonline.
How did you get started withthat?
How did you I know that youstarted with George uh Siemens,
right?
Siemens?
Stephen (03:21):
George Siemens, yeah.
Um, so I've always been onabout networks, and that goes
back to my days studyingphilosophy.
And back then I I representeduh knowledge as being based in
similarity and association, andthat led me to something called
(03:43):
connectionism, which was aphilosophy or approach in
computer science, uh, which wasbased on artificial neural
networks, which totally meshedwith what I was thinking at the
time.
And so uh, as I developed myown thinking about online
learning and onlinecommunication generally,
(04:06):
networks fell naturally intoplace because, of course, we
were on the internet works,right?
Speaker 00 (04:12):
Right, right.
Stephen (04:13):
Um so George Siemens came along with an article
titled Connectivism, where hedrew out some uh features of
networks and applied them tolearning, uh, which totally
meshed with what I was thinking.
And um, so consequently, Georgeand I got together.
(04:35):
He held a conference calledConnectivism.
It was an online conferencehugely popular.
So we're sitting in a bar oneday in Memphis, believe it or
not, and we decided to offer anonline course about
connectivism.
Um and we decided that thecourse shouldn't just try to
(04:58):
explain connectivism, it shouldtry to model connectivism.
Um, and that was a good thingbecause we had 2200 people show
up.
And because we designed thecourse as a network and not one
of your standard, you know, uhpeople sign up for a discussion
(05:18):
list or whatever, the courseworked.
We could accommodate that manypeople, and that prompted Dave
Cormier to call it a massiveopen online course, and thus the
MOOC was born.
Chris (05:31):
Wow, wow.
And there are many forms, as Ialluded to before.
What are effective MOOCs forthese online?
Which ones do you findpersonally grab not only you
know the content that you wantto, you know, eat, consume, but
also have that personalconnective um aspect to them?
(05:55):
What are some of the componentsthat you would look for in a
MOOC that would allow thatconnectivity to happen?
Uh these days, um, and you haveto keep in mind, right?
We're we're almost 20 yearsfrom the first MOOC.
That was in 2008.
So these days, I would say itneeds to be in some way
(06:20):
federated.
Back then, I would have saiddistributed, and I still mean
the same thing when I use thetwo different terms.
And what I mean by that is itshould not be based in a single
place.
Um, and just as an aside,that's what the commercial MOOCs
all did is they were one bigwebsite with a whole bunch of
(06:44):
Amazon uh AWS backing forstorage and stuff and AI to do
the marking and that, but theywere all just centralized, and
none of those models survived.
One by one, they all ended upselling out one way or another
and becoming closed products.
(07:05):
But a massive open onlinecourse really ought to be a
network, of course, right?
Not one site, but manyconnected sites.
And the properties of a MOOCfollow from that thinking,
right?
Massive because networks cansupport mass much better than
(07:28):
single centralized services.
Open because it's not a networkif it's closed, right?
Open means it can join andleave, that content can come in
and come out and flow freelythroughout, right?
Online, because well, that'sthe easiest place for us to have
networks.
And the only thing that'sdifferent about a MOOC, um and
(07:53):
just uh a network generally isin the word course, and here
course implies kind of twothings.
First of all, a fixed startdate and fixed end date, so it's
it's a moment in time, andsecond, some kind of well, I
(08:18):
used to talk about going back tothe original Oxford-Cambridge
model where the learning was infact all organized by the
students who would gather aroundthe professors, and each
student would have a professoras a mentor.
But what they would do is theywould convince, and they had
(08:39):
that this they actually had todo this, they had to convince a
professor to offer a course oflectures, where course in this
case means series of lectures.
And so that that would form thecore of the MOOC, right?
There'd be a course ofdiscussions on a sim on a single
(09:01):
topic, uh, you know, hosted byone of us and bringing in
people, and then the wholenetwork would surround that and
discuss that, which is what thestudents back in the
Oxford-Cambridge model did.
So it's taking that model andapplying it to the digital age.
That's interesting.
(09:21):
Wow.
And do you feel that when we'relooking at these, like how the
technologies, the ethics, theprivacy of all of this, like,
because you do say it's open,right?
It's an open um network, youcan come and go.
Like more and more we'reconcerned at schools anyway, and
(09:42):
like school boards, and youknow, they're worried about the
ethical use and the privacy use.
Like, how does how would thatcontour or cause a problem with
it being used more prolificallythroughout, example, our youth
sector education, like for highschool students, example?
Stephen (10:02):
Well, yeah, I mean, it depends on what your view of
education is.
Um I'm just trying to thinkabout how to word this
diplomatically, because uh, youknow, a lot a lot of people are
very concerned about controllingeducation, controlling the
content of education, um, andthey're very concerned about
(10:25):
education imparting specificknowledge and specific content
and even more specific values,especially cultural values,
right, on students.
Um and that's not really themodel that a MOOC follows.
Um a MOOC is much more aboutthe students themselves
(10:49):
determining for themselves wherethey want their learning to go.
Um, George and I used to say,and I still say, you know, the
content, quote unquote, is aMacGuffin.
Right?
Uh it's the thing that we'retalking about, but it's not the
thing that we expect people tolearn.
(11:11):
Uh you know, it doesn't matterif they learn the content.
The content is just thestarting point.
It's the seed, it's thecatalyst, it's the thing that
gets people going.
Um, and what they're actuallydoing in our course and in any
course is exercising their mind,exercising themselves, thinking
(11:35):
about these topics, workingthrough these topics, hopefully,
and we really encouraged this,creating and writing about these
topics.
Right?
Um so it didn't matter whetherthey learned the topic, what
they what they were learningwere the skills needed to deal
(11:57):
with kind of things like this.
So uh you know, it it's adifferent way of thinking about
learning.
Um coming into a massive onlinecourse, you know, we we talk
(12:17):
about things like privacy andthings like that.
Um my courses, um, I don't evenask for registration.
I don't want to I mean I guessI'm kind of curious, but it
doesn't matter to me whether ornot I know who or how many
(12:39):
people have signed up.
Right?
I mean this this is you knowthe the the need for
registration isn't the same asthe need for learning.
There's incredible for you.
Um, you know, so uh if I wantedto sign up for a mailing list,
then I'd need their emailaddress, otherwise I wouldn't
(13:00):
know to send where to send thenewsletter, but they could just
as easily use the RSS feed andaccess to that as anonymous or
as anonymous as it gets, right?
Speaker 01 (13:11):
Right, right, right.
Stephen (13:12):
So there's much less concerns about privacy and
surveillance.
A person's contributions to aMOOC are are not required by the
curriculum, right?
They're voluntary.
Each person contributeswhatever they feel comfortable
contributing.
Speaker 01 (13:31):
Okay.
Stephen (13:32):
So if the person comes into the course having a basic
understanding of you know notsharing too much on the
internet, then you know there isa prerequisite there.
Um then from privacyconsiderations they should be
fine.
Um so you know these kind ofquestions don't arise nearly as
(13:58):
much in student, I don't want tosay student managed because
that's not quite the right word,right?
But and and and not evenstudent-led, perhaps
student-driven might be a betterword, but even that's not quite
the right word.
But you get the idea, right?
Uh these questions don't arisethe way they do in a course with
(14:21):
the prescribed curriculum,specific content that a student
must learn, specific activitiesthat a student must undertake,
specific rules of participationthat a student must follow,
right?
If you remove the word mustfrom learning, uh a lot of the
(14:42):
questions about privacy,security, et cetera, disappear.
Chris (14:45):
Interesting.
Interesting.
And I mean, one of the thingstoo that kind of struck me when
you're when you're speaking wasthat this need for content that
we have in structured schooling,right?
It's all driven by content.
We test it, uh, you know, wehave goals you have to set.
And we often forget about theperson behind all of that and
(15:08):
those skills that we need todevelop in school and not just
content.
I mean, I think about school, Imean, uh what I remember is my
personal interactions and playsand sports I did.
I don't really remember whathappened in C4Math at all.
Stephen (15:27):
Well, we don't know how to add, but that's basic
numerous, you know.
Chris (15:32):
I mean, they say grade eight is probably about as much
as you need to survive in thisworld.
Where do where does where do weconnect those two from the
content to the person?
And also help in thatdevelopment not only of the
knowledge of this of whoever isinteracting with the MOOC, but
also how do we develop thempersonally and their skills and
(15:55):
their critical thinking, theiryou know, creativity, etc.
Yeah, I mean, I know those areimportant questions.
Uh the last thing we want uh isto have a generation of
students who are incapable offunctioning, especially in a
(16:18):
modern industrial informationage economy.
Um, we we can look at anycountry where the education
system has collapsed or beencompromised to see the results
of that.
Um we we don't want that tohappen, obviously.
(16:38):
Um since the very beginning ofmy work in educational, I've
urged caution.
And I still urge caution,right?
You don't jump into somethingwhole hog before you know what's
gonna happen out the other end.
unknown (16:55):
Right.
Stephen (16:56):
Um which is unfortunately what I think we do
sometimes.
Uh I tend to have two thingsthat are are my my guiding
points here when I when I thinkabout this question.
Um the first guiding point isif a thing is fundamental, it's
(17:21):
gonna show up as soon as you tryto do something.
Right?
Think about that.
Yeah, uh think about anythingsimple, like we're having a
conversation, right?
The need for language is gonnacome out, right?
Um, you know, the better we arewith language, the better the
(17:43):
conversations we're gonna have.
And that that's going to betrue with a lot of things.
If if a if a person wants to, Idon't know, um sew dresses,
like pull that out of the air,right?
They're gonna have to learn toread.
They're gonna have to learn towork with patterns and
(18:05):
directions.
The more they get into it, themore these fundamental skills
will become important.
Measurement, mathematics, area,geometry, uh, 3D geometry if
they're doing fashion, right?
Uh you know, all of these willcome into play.
(18:26):
And and a whole bunch of othersubjects, right?
Chris (18:29):
But you know, so the fundamentals your your starting
point is the hook.
Where where are they gonna findentry into whatever you're
asking them to do?
Right, right.
Stephen (18:42):
The fundamentals will emerge, right?
Right.
So it's it's our role aseducators to make sure that the
resources are there in place forthem to learn these when they
need them, but you know, forcingthem on them, probably not the
best way.
And that's leads obviously, andyou've already alluded to it.
(19:04):
The second point is what is itthe person wants to do?
Right.
So a couple, there's there's acouple of caveats there, right?
Because you know, uh peoplesay, well, all this all the
students, all the kids want todo is play.
Well, strictly speaking, thatmight be true, but if you look
(19:27):
at what play amounts to, in manycases it amounts to imitating,
uh imitating what they seearound them.
Uh so you see them playing alot of sports.
Why?
Well, they see sports on TV,uh, you know, uh, or they see
the older kids playing sports orwhatever.
You know, so uh you kind ofneed an environment that's
(19:51):
inspiring that will give kidsideas what they want to do,
right?
Um and you need to provide therole models for them.
Um, and and and then the accesspoint into whatever it is they
want to do.
Some kids will want to playbasketball all day, every day.
(20:14):
Um should this be encouraged toa certain point, sure, why not?
Um, you know, they they will bedeveloping themselves as
athletes.
And you know, again, the betterat being an athlete, you're
going to need to learn a bunchof stuff, right?
(20:37):
It's not just gonna be aboutthe physical performance.
You're gonna want to getbetter, you're gonna want to
learn about arts and and andphysics, uh, and tactics, uh,
and physical fitness and all ofthat.
Right.
Is it is it okay for a personto focus on that in their life
(20:57):
for the rest of their life?
Sure, why not?
They don't need to be a workerin a factory these days.
Nobody needs to be a worker ina factory, uh, it's all
potentially automated.
So that that's really gonnatake, you know, and and uh so
other kids will want to makerobots.
I would have been one of thosekids, except my hands were not
(21:20):
very good.
But you know, maybe, maybe wellno, I played with Mechano and
things like that, and I was allso yeah, that that never really
worked, but but computers wouldhave been a natural for me, and
were a natural once I had accessto them.
Right, right.
And so but the thing is, right,to a large degree, kids can
(21:48):
follow their passions and itwill lead to a good outcome.
And then then we can deal withthe exceptions.
There is there will probably beexceptions, but if you think
about it, dealing with theexceptions and guiding them so
that you know they actually dodevelop worthwhile skills and
(22:10):
and abilities is better thanstamping the same quote unquote
foundational knowledge on all ofthem, in my opinion.
Chris (22:20):
Right, yeah, totally, totally.
Right, right.
But still kind of satisfyingthe role that they are they
their interest guiding them to,and then kind of pulling in all
(22:42):
this stuff as you're goingdiscovering, you have no choice,
right?
Yeah, that's reallyfascinating.
I love, I love there's somegreat, great quote quotes in
there, Steven, for sure.
Stephen (22:53):
And and just as yeah, this is not none of this is my
unique idea, right?
There's there's a lot of peoplewho have said this before me.
Um everyone from Frieri toIllic to uh uh well John Holt
would have mentioned it.
Um other names.
(23:16):
Um there's there's one personin specific, I can't remember
his name, but oh uh dang, it'sright on the took of my tongue.
I have Alvin Toffler, butthat's not it.
Chris (23:29):
Well, maybe it will by the end of the day.
It'll come back.
Boom!
Speaker 02 (23:34):
It'll come back.
Chris (23:35):
Steven in walks AI, right?
Now we know since November2022, things have dramatically
changed, not only in education,but the world is feeling these
waves coming through.
We know it's not goinganywhere.
How do you see AI supportingyou know online learning
(23:58):
connectivity?
Like, is it gonna be an assetto us?
And what do we have to get ourheads around or beyond?
Because I know a lot of thetimes right now in in education
is kind of seen as well, it's acheating machine, you know, it's
like a quick way to get arounddoing the work where there's no
critical thinking.
And where do you stand on AI?
(24:19):
Like, what what do you what areyour hopes and also kind of
your cautions about you knowgoing all in with AI in in in in
in learning?
Stephen (24:29):
Yeah.
So well, again, first of all,be cautious and conservative,
you know, you know, like don'tjust go all in on AI because you
don't know what you're goingall in on.
But that's it.
Okay, that's the first thing.
Second thing is AI is notmagic, it's math.
(24:50):
Okay, that's why it's not goingaway.
So if you know, you you cancriticize open AI and meta and
anthropic all you want, andthere's lots of reasons to
criticize those companies.
The fact itself of the math isnot going away because the math
(25:10):
is networks.
The networks is the stuff thatI've been talking about my
entire life, right?
Uh that's not going away.
AI is based on connections,it's based on strength of
connections between individualunits in a network.
Um, and in large languagemodels, it's based on strength
(25:32):
of connections between words orperhaps parts of words.
And in some of the more uhrecent AR systems, strength of
connections between words andphrases and longer sentences,
increased attention, if youwill.
So none of that's going away,none of that is magic.
(25:55):
Okay.
Uh it does point to um howirrelevant the content is.
Right.
Chris (26:05):
Um I think that's the big exposure too, right?
That yeah, I mean, we thoughtwhen when kids had handhelds in
Google, yeah, forget about it.
I could go and like ask theteacher a question now, and
they're suddenly the rulesreverse.
I think AI like trug liketriples that.
Stephen (26:24):
Yeah, yeah.
So now, interestingly, um,because you know, artificial
intelligence, properlyso-called, originated in a
research project to try toemulate human reason using
computers, right?
Hence artificial intelligence,right?
(26:46):
Um and it has been more or lesssuccessful.
The part that hasn't reallychanged is emulating humans, and
many of the human foibles arefound in artificial
intelligence.
Um, for example, confidentlyasserting something that is not
(27:09):
true as being true.
Humans do that all the time,right?
Sometimes on purpose, which wecall lying, sometimes by
accident, um, which we call, Idon't, I don't think we even
have a name for it.
Um, you know, um depending onhow they're set up, an AI system
(27:31):
might be a sycophant, mightmight say, oh yes, you're so
correct, and all of that.
So you you can't just acceptwhat an AI says as necessarily
true.
Um now that should have beenthe case all along, um, but
(27:55):
especially well, I was gonna sayespecially recently, but I
suppose it's something that'salways been the case through
human history.
Uh, we've been taught to acceptwhat the authority says as true
and objectively true, um,especially in the age of
science.
Um, but you know, even in theage of faith, I guess.
(28:18):
Um and of course, that shouldnever have been the case.
We should always have beencritically reflective of what
anyone tells us.
And uh, you know, the thescientific method, properly
so-called, basically is a set ofmechanisms that enables a
(28:40):
person to determine whether ornot something that is said to be
true is in fact true.
And it involves um, you know,trying it for yourself, getting
the same information frommultiple sources, seeing if it
stays true over time, um,imagining counterfactuals and
(29:03):
falsification and testing andyou know, those sorts of
principles, right?
And we can be really loose andfuzzy about what those
principles are, and they'llstill more or less work.
So there's a strong correlationbetween what we think of as
scientific method and what wethink of as critical thinking,
although, bracketed aside,there's a whole industry of
(29:27):
false critical thinking.
Um beginning with De Bono andcontinuing on.
Not to say that everything deBono says is false.
There are ways of broadeningyour creative and imaginative
(29:53):
powers, not determining whetheror not something somebody tells
you ought to be believed.
See the distinction, right?
Speaker 01 (30:01):
So yeah.
Chris (30:02):
Um, and so you have you need to be kind of careful about
that when we talk about whatwhat we mean by critical
thinking.
I've I've seen thatmisunderstanding play out.
unknown (30:13):
Okay.
Speaker 02 (30:14):
So, um, so how do you teach critical thinking?
Well, not as a subject.
Speaker 00 (30:21):
Right.
It's again kind of a livedexperience.
Like you get better at it bydoing it.
Speaker 02 (30:27):
You get better at it by doing it, and you do it,
especially when you're a kid,when you see other people doing
it.
Speaker 01 (30:37):
Right.
Stephen (30:37):
Right.
A big part, a big part of mywork over the years has been to
try to offer a model of what Ithink critical thinking is.
And I have my daily newsletter.
Yes.
And each article that I review,I give it a little bit of
(31:01):
critical thinking, right?
So you read my newsletter, yousee uh four, five, six, eight
items of me doing the criticalthinking thing on an article.
Um, you know, and in my longerwork, I try to do longer
instances of critical thinkingand develop the whole thing.
Um that's part of the answer.
(31:22):
The other part of the answer islet's think about how how do I
want to put this?
Um let's think about whatthinking is.
Let's think about what evencritical thinking is, not in
terms of a process or a method,but in terms of how we know
(31:46):
what's true.
No, no, I don't even want tosay that because truth is an
attitude we have toward aproposition.
It's what we call apropositional attitude.
Truth is a label that we giveto a sentence in a language, but
(32:06):
our our thinking doesn't workin languages, right?
Uh we hear languages when wethink, but that's our perception
of ourselves thinking.
What a mess.
Chris (32:17):
What a mess.
What we do is what the computersdo.
We recognize patterns.
Right.
Stephen (32:27):
All right.
So what we want to model ispattern recognition, pattern
testing, all of that.
Um there's a whole story Iwould tell.
And you know, if we we thinkabout the different types of uh
logic and critical thinking,these are different forms of
(32:48):
pattern recognition.
That's why Sesame Street got itright when they did this whole
one of these things does notbelong.
They're after patternrecognition.
Chris (33:01):
Great.
Could that be like that's apart of computational thinking,
right?
Like where you have certainpattern recognition, like
there's certain mathematicalconcepts that are more
philosophical in a sense, butthey're hemmed to you know, math
and science and like reality.
(33:22):
Um I I've always been reallyfascinated with that mindset
instead of a math thinking.
Like, I wish instead of math,you would have taught me more
about computational thinking, sothat I start to understand
patterns and connections betweenthings and sequencing, and
which which, like you said, isrunning not only our
(33:45):
technologies, but AI is um Imean it's a prediction machine
in in essence.
Stephen (33:51):
Well, yeah, uh absolutely so is the human
brain.
It's a prediction machine.
Uh well the but it's also likea super sensitive uh content
(34:11):
receiver, uh signal receiver,you know.
Like uh, you know, we have wehave the various senses in that,
but it's like our head is ourantenna, right?
It's well, our whole body isthe antenna, right?
So we're very sensitive to theenvironment and we and and
(34:34):
signals, causes, sensations,whatever, come in, and then
we're very sensitive to whatthey are, and then we look at
the patterns in thosesensations.
Um, and some of the patternsare sounds, and some of the
patterns are words, and so on.
Chris (34:53):
Um fascinating, Steven.
That's like you're making methink of things in different
ways here.
I love this.
Stephen (35:00):
And we go back to computational thinking.
Uh we think about whatcomputational thinking is, and
it's fascinating if you thinkabout it.
Uh, what is it?
Right.
So what what's a computerprogram?
Computer program is a set ofdeclarative statements, perhaps.
(35:21):
Uh, but really the the magic ina computer program works um in
the form of conditionalstatements.
Right.
Okay.
If this then that.
And and the rest of computerscience, um, as any logician
will tell you, is logicallyequivalent to a set of
conditional statements, right?
A loop is a type of conditionalstatement, right?
(35:45):
Uh a series is a type ofconditional statement, right?
If you've done this, then dothis.
If you've done this, then dothis.
What is a conditionalstatement?
Conditional statement is afascinating thing.
I spent a long time studying itin my youth from a
philosophical perspective.
My first published paper everwas in fact called Conditional
(36:10):
Variability.
Um and what makes a statementif X then Y true?
Right, and we think it's justyour standard truth table, but
there's so much around a wholecontext around that.
Ultimately, what a conditionalstatement is is a pattern
(36:32):
recognizer, right?
If something is the case,right?
So, but pattern recognizersdon't, and this is the this is
where neural networks were sobrilliant.
Pattern recognizers don't haveto recognize patterns exactly.
Speaker 01 (36:52):
Okay.
Chris (36:53):
Um and and you know is that some of the bit of like
with AI, like withhallucinations sometimes, like
they'll want to satisfy, but itmight not fit exactly to that,
but they'll work their wayaround it somehow.
Stephen (37:12):
Have you ever walked in a crowd and thought you
recognized someone and then itturned out it wasn't the person
you thought you recognized?
Chris (37:20):
Totally, totally, yeah.
So that's that's a good, goodconnect.
That's a great example, Steve.
Wow, yeah, totally.
Stephen (37:29):
That's exactly what's happening with a hallucination,
right?
Um, you and this is a reallyimportant point.
You're predisposed to recognizea certain set of things, right?
Right?
People in your family, uh,famous people, um, people who
(37:49):
look different, uh whatever,right?
I mean it's the set isdifferent for everyone, right?
Um and I'm pretty sensitive tothis because my eyes are so bad
that I'm really bad atrecognizing people, horrible at
it.
And I've insulted so manypeople over the years as a
(38:11):
result.
I'm just but you know, in theabsence of something, you feel
its presence keenly.
Um, but you can recognize on apartial pattern, right?
That is, in other words, youcan recognize things based on
similarity rather than identity.
But what makes similarity worksis this precondition, or as we
(38:38):
say these days, context.
Right?
So what is computationalthinking?
It is recognition bysimilarity, what counts as
similarity, context, and so nowyou're into topics, you know,
(38:59):
how do you do pattern matching?
Um, how do you doclassification, categorization,
but not in terms of necessaryand sufficient conditions, but
in terms of similarities orpartial representations.
What is a representation?
What counts as arepresentation?
(39:20):
If I told you this is uh KarimAbdul Jabbar, first of all,
let's check.
Steven's holding up a he'sholding up a spoon.
Chris (39:29):
Yeah, you recognize the name, right?
Speaker 00 (39:32):
Yeah.
Speaker 02 (39:32):
Okay, good.
Just checking.
Chris (39:34):
Totally, totally.
I'm just because this is justgonna be audio.
And so Steven was holding up aspoon and asked me that
question.
Stephen (39:43):
And now if I do this, yeah, I just conveyed a signal
to you that you can probablyinterpret.
And again, nobody on the audiosaw that.
I bounced the spoon a bit andmade it leap up toward a basket.
Speaker 01 (40:02):
Right.
Stephen (40:03):
There was no actual basket, but you would have
inferred that there was a basketthere.
Now, you know, and I know therewasn't really a basket there,
but that was never the issue.
The issue was did you correctlyinfer that I intended you to
infer that there was a basketthere?
(40:23):
That's computational thinking.
So it's not about followingrules and principles and
constructing algorithms.
It's about pattern matching,data awareness, um,
classification, categorization,representation, those kinds of
(40:46):
topics.
And those are the kinds ofskills that are fundamental in
an information age, not content.
Chris (41:01):
Right, right.
Where we are now, which is um II I love I love your words,
Stephen.
And I mean, the reflectionsthat you're you're making me and
I know the listeners have isjust out of this world.
I really, really appreciate thetime.
I know that we're running a bitlong, but I just this is so
(41:23):
fascinating to me.
I mean, we could just keepgoing and going about this, but
I do want to respect our time.
I'm gonna have you back,Steven.
It would be great to just havea follow-up on this because it's
so um, I love how philosophyideas connect with tech.
I mean, I love that marriage orthat that relationship that
(41:45):
they have.
Um, so I'd love to dive deeperinto that.
If you want more of Steven, hedoes, as he alluded to, OL
Daily.
It's a great newsletter.
You can find it on LinkedIn oron his site, which I'll put in
the descriptor, um, so that youhave access to all these great
um ebooks he has, and also, likehe said, his reflections, his
(42:05):
critical thinking on articles,etc.
And he's he's prolific ingetting these out, and they're
really good.
So I recommend those to uh youlisteners as well.
Steven, this has been anabsolute pleasure.
Uh it's been such um I I I needto sit and just think a little
bit with everything that we'veexchanged today.
(42:26):
Um, but I would really love touh continue this one day.
Stephen (42:30):
Sure, it'd be my pleasure.
Happy to do it.
Chris (42:32):
Amazing.
Well, Steven, you have yourselfa great day.
And again, thanks for joiningus and and sharing some of your
knowledge with us.
I think we're all that muchsmarter today, now after this.
Stephen (42:42):
So thank you.
Oh, you're very welcome.
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