1
00:00:00,000 --> 00:00:03,084


2
00:00:03,084 --> 00:00:05,500
GEORGE SIEMENS: So if you were
to look at the field today,

3
00:00:05,500 --> 00:00:08,530
just sort of where it's
gone over the last 20 years,

4
00:00:08,530 --> 00:00:13,060
where it sits now with open
education shifting, more so from just

5
00:00:13,060 --> 00:00:17,350
content to the mappings of
that content to outcomes,

6
00:00:17,350 --> 00:00:21,160
to the use of learning analytics to do
a better job of producing or creating

7
00:00:21,160 --> 00:00:23,410
an impact for students
and student success,

8
00:00:23,410 --> 00:00:26,080
and to evaluate just the
cognitive development,

9
00:00:26,080 --> 00:00:29,230
the institutional influence,
and so on of analytics,

10
00:00:29,230 --> 00:00:32,650
where would you say your
primary concerns are?

11
00:00:32,650 --> 00:00:36,190
Even if you were to articulate my top
three concerns for the open education

12
00:00:36,190 --> 00:00:38,706
movement going forward are--

13
00:00:38,706 --> 00:00:41,787
DAVID WILEY: Oh, so you've
already mentioned the way

14
00:00:41,787 --> 00:00:44,620
that publishers have kind of ceded
the battle to try to win the war.

15
00:00:44,620 --> 00:00:49,420
So the war is really going to
be on platform, not on content.

16
00:00:49,420 --> 00:00:52,780
And content was an easy battle
for the community to fight,

17
00:00:52,780 --> 00:00:55,000
because anybody with a
PhD in the discipline

18
00:00:55,000 --> 00:00:58,360
could sit down with a word
processor and create something

19
00:00:58,360 --> 00:01:02,200
that had a hope of being competitive
with what other textbooks somebody else

20
00:01:02,200 --> 00:01:03,870
might be using.

21
00:01:03,870 --> 00:01:08,190
When you talk about trying to replicate
platforms for personalization analytics

22
00:01:08,190 --> 00:01:11,730
and things like that now, the team
of people in the open community who

23
00:01:11,730 --> 00:01:14,490
can really compete head to head
with what the publishers are doing

24
00:01:14,490 --> 00:01:16,560
narrows drastically.

25
00:01:16,560 --> 00:01:22,830
And I think in as much as these
assessments and competency

26
00:01:22,830 --> 00:01:25,650
statements and content,
if those are all open,

27
00:01:25,650 --> 00:01:28,980
I think it makes total sense for them
to be placed into a variety of platforms

28
00:01:28,980 --> 00:01:33,930
to see, how can this platform's
special sauce make it more effective,

29
00:01:33,930 --> 00:01:36,300
or give a different
perspective or a view on it?

30
00:01:36,300 --> 00:01:39,672
But at some point, it seems like
the content, the assessments,

31
00:01:39,672 --> 00:01:42,630
and the competency statements start
to become less and less interesting

32
00:01:42,630 --> 00:01:45,270
as they're kind of
subordinated to the platforms.

33
00:01:45,270 --> 00:01:49,254
And if all the platforms are
proprietary, that's a problem.

34
00:01:49,254 --> 00:01:51,420
GEORGE SIEMENS: Well, and
you consider too the issue

35
00:01:51,420 --> 00:01:53,880
with networks that we don't
hear about often enough,

36
00:01:53,880 --> 00:01:57,840
is that networks don't
produce many winners.

37
00:01:57,840 --> 00:02:01,800
On a given day, we look at
early on, I remember mid-2000

38
00:02:01,800 --> 00:02:05,910
when social media wasn't called that,
at that point we called it web 2.0.

39
00:02:05,910 --> 00:02:07,680
But there was a mess of sites.

40
00:02:07,680 --> 00:02:11,700
You'd be on Ning one day, and then
the next day you would be on--

41
00:02:11,700 --> 00:02:12,840
DAVID WILEY: Friendster.

42
00:02:12,840 --> 00:02:16,200
GEORGE SIEMENS: Friendster,
early stages of Facebook.

43
00:02:16,200 --> 00:02:19,500
I remember at University of Manitoba,
we had built our own social network

44
00:02:19,500 --> 00:02:21,130
for students to interact.

45
00:02:21,130 --> 00:02:24,970
And so you worked with
dozens of platforms.

46
00:02:24,970 --> 00:02:28,830
And yet today if you want to connect to
reach out to people, you're on Twitter,

47
00:02:28,830 --> 00:02:33,090
you're on Facebook, you're on
Instagram, you might be on Pinterest,

48
00:02:33,090 --> 00:02:36,360
and you might be on Snapchat
if you figure out how it works.

49
00:02:36,360 --> 00:02:37,327
And that's it.

50
00:02:37,327 --> 00:02:38,160
So that's the issue.

51
00:02:38,160 --> 00:02:42,300
And I think publishers, we're going to
see some similar network effects where

52
00:02:42,300 --> 00:02:45,570
the platform owners for adapted
and personalized learning,

53
00:02:45,570 --> 00:02:49,800
unless there is a concerted
attempt to create a vision,

54
00:02:49,800 --> 00:02:52,650
and to create an open landscape
where that's possible,

55
00:02:52,650 --> 00:02:58,050
we're going to see sort of a
Facebookization of open education

56
00:02:58,050 --> 00:03:03,210
as well, which is in the long run,
why create for x number of platforms

57
00:03:03,210 --> 00:03:06,180
when 98% of your users are here?

58
00:03:06,180 --> 00:03:10,470
Or just consider mobile phones now.

59
00:03:10,470 --> 00:03:13,200
You're designing for Android,
you're designing for Apple.

60
00:03:13,200 --> 00:03:15,600
Who else are you designing for?

61
00:03:15,600 --> 00:03:18,480
You captured the majority
of your audience there.

62
00:03:18,480 --> 00:03:21,021
And so I think when I'm concerned,
when I look at the future,

63
00:03:21,021 --> 00:03:24,150
I'm concerned exactly that is
that the platform effect, which

64
00:03:24,150 --> 00:03:26,100
gives us a lot of really good benefits.

65
00:03:26,100 --> 00:03:29,040
It's so easy to log into Facebook
and see what everybody's up to.

66
00:03:29,040 --> 00:03:32,040
You join a new service, you just
use your Facebook or your Google ID

67
00:03:32,040 --> 00:03:33,220
to go onto the service.

68
00:03:33,220 --> 00:03:34,380
It's easy.

69
00:03:34,380 --> 00:03:38,970
And easy sometimes is a bit of an enemy
for openness, because it's easy for me

70
00:03:38,970 --> 00:03:42,210
as a faculty member, if I'm
not paying for that textbook,

71
00:03:42,210 --> 00:03:46,050
to just use a textbook that gives me the
PowerPoint resources and away you go.

72
00:03:46,050 --> 00:03:49,347
So the platform issue is a
significant one to look at.

73
00:03:49,347 --> 00:03:51,180
Is there anything else
you're seeing though,

74
00:03:51,180 --> 00:03:54,240
that you find alarming or
disconcerting, or even just areas

75
00:03:54,240 --> 00:03:57,386
where you think your main focus
will need to be going forward?

76
00:03:57,386 --> 00:04:01,530
DAVID WILEY: You know, I'm so focused
on the platform problem right now.

77
00:04:01,530 --> 00:04:05,730
It's almost kind of hard to see past
it, because if you lose on platform,

78
00:04:05,730 --> 00:04:07,905
it's hard to see how you
can win on anything else.

79
00:04:07,905 --> 00:04:12,612
GEORGE SIEMENS: So can you explain
how Lumen is a platform problem?

80
00:04:12,612 --> 00:04:14,820
You mentioned this earlier
in one of our discussions.

81
00:04:14,820 --> 00:04:18,029
But now that we've had a few weeks
under our belt with the content

82
00:04:18,029 --> 00:04:21,750
and we're moving toward wrapup, I
think hopefully course participants

83
00:04:21,750 --> 00:04:26,370
will see why that platform
view is such a concern for you.

84
00:04:26,370 --> 00:04:29,040
But can you talk a
little bit more about how

85
00:04:29,040 --> 00:04:32,880
you're addressing the platform problem
to your current projects, namely Lumen?

86
00:04:32,880 --> 00:04:34,420
DAVID WILEY: Yeah.

87
00:04:34,420 --> 00:04:38,640
So the first, most obvious answer
is that the overwhelming majority

88
00:04:38,640 --> 00:04:40,210
of the platform is open source.

89
00:04:40,210 --> 00:04:43,080
It's a GitHub, you can grab it
and check it out and play with it.

90
00:04:43,080 --> 00:04:47,040
And you could stand up a
Lumen competitor tomorrow,

91
00:04:47,040 --> 00:04:49,870
between the open content and
the open pieces of the platform.

92
00:04:49,870 --> 00:04:52,120
There are pieces of the
platform that aren't open yet,

93
00:04:52,120 --> 00:04:55,770
but I think watch that space.

94
00:04:55,770 --> 00:05:01,549
On the black box algorithms
side, I think there are some--

95
00:05:01,549 --> 00:05:03,340
I don't know if they're
useful, but I think

96
00:05:03,340 --> 00:05:06,000
at least interesting attempts
that we've made there

97
00:05:06,000 --> 00:05:09,840
that for example, in dashboards
presented to a faculty member,

98
00:05:09,840 --> 00:05:12,840
this is George here, here are
the five students in your course

99
00:05:12,840 --> 00:05:15,230
that need the most attention right now.

100
00:05:15,230 --> 00:05:17,970
Reach out to them.

101
00:05:17,970 --> 00:05:22,307
You'll see that our approach is very,
very light on visualizations of graphs

102
00:05:22,307 --> 00:05:23,640
and charts and things like that.

103
00:05:23,640 --> 00:05:29,640
And it's very heavy on expository prose
that explains to you, when it says,

104
00:05:29,640 --> 00:05:32,767
here are five students who
are trying but struggling.

105
00:05:32,767 --> 00:05:35,850
Then there will be a short paragraph
beneath that that in English language

106
00:05:35,850 --> 00:05:40,530
describes what algorithm, and how the
algorithm functions that made people

107
00:05:40,530 --> 00:05:42,130
land on that page.

108
00:05:42,130 --> 00:05:46,150
So for example, these students
all have done the reading.

109
00:05:46,150 --> 00:05:48,150
But they failed their
first attempt at the quiz.

110
00:05:48,150 --> 00:05:51,229
And they're not engaging with the
self-checks and the other opportunities

111
00:05:51,229 --> 00:05:52,020
that are available.

112
00:05:52,020 --> 00:05:55,850
So the way that that gets
implemented in software

113
00:05:55,850 --> 00:05:59,380
is described in plain
language for faculty members

114
00:05:59,380 --> 00:06:02,300
so that even if you don't
have the technical capability

115
00:06:02,300 --> 00:06:06,320
to go in to audit the code itself,
you do have some understanding of,

116
00:06:06,320 --> 00:06:08,570
here's why people are showing up here.

117
00:06:08,570 --> 00:06:11,540
GEORGE SIEMENS: So at what
level do we start to say,

118
00:06:11,540 --> 00:06:16,220
you can't be an educator today if
you're not a data scientist, lite

119
00:06:16,220 --> 00:06:18,230
or a programmer lite person?

120
00:06:18,230 --> 00:06:22,340
Are we at that stage where
we're discussing here the fact

121
00:06:22,340 --> 00:06:26,000
that the content which is
traditionally our practice as educators

122
00:06:26,000 --> 00:06:28,130
has been about developing people.

123
00:06:28,130 --> 00:06:30,479
We use content as a primary
development mechanism.

124
00:06:30,479 --> 00:06:33,020
There was a knowledge component,
but there's also a mentoring

125
00:06:33,020 --> 00:06:36,200
and a beingness to that experience.

126
00:06:36,200 --> 00:06:42,020
Are we now at a stage though,
where some level of meta-cognition

127
00:06:42,020 --> 00:06:46,760
about the analytics, and about
the content that's being used,

128
00:06:46,760 --> 00:06:49,730
and how models are being created
for students where that needs

129
00:06:49,730 --> 00:06:53,780
to be a part of our education practice
in any course, much like in the past,

130
00:06:53,780 --> 00:06:55,820
let's say critical
thinking has been embedded

131
00:06:55,820 --> 00:06:57,370
in all aspects of the curriculum?

132
00:06:57,370 --> 00:07:00,520
DAVID WILEY: Well, that's certainly
a problem that we think a lot about.

133
00:07:00,520 --> 00:07:02,810
And we think about it even
a little more expansively

134
00:07:02,810 --> 00:07:04,940
than that in the
context of our analytics

135
00:07:04,940 --> 00:07:08,330
work, because again, our analytics
work is mostly forward facing

136
00:07:08,330 --> 00:07:13,280
to the kind of faculty member that will
end up at a community college, that'll

137
00:07:13,280 --> 00:07:16,460
end up teaching a 5-5 or a 6-6.

138
00:07:16,460 --> 00:07:18,904
And it comes from a
particular academic background

139
00:07:18,904 --> 00:07:21,320
that's not the same background
you'd have if you're at MIT

140
00:07:21,320 --> 00:07:23,450
or if you're at Stanford
or someplace like that.

141
00:07:23,450 --> 00:07:26,660
And we really do think about it
very much as this faculty member has

142
00:07:26,660 --> 00:07:29,090
no background in the learning science.

143
00:07:29,090 --> 00:07:31,384
They have no background
in instructional design.

144
00:07:31,384 --> 00:07:33,050
They have no background in data science.

145
00:07:33,050 --> 00:07:36,450
And they have no background in the
behavioral economics of it either.

146
00:07:36,450 --> 00:07:39,710
And that in some ways, or in many
ways, is the most interesting piece,

147
00:07:39,710 --> 00:07:43,440
because you can understand
what needs to happen.

148
00:07:43,440 --> 00:07:45,980
You can understand the
learning science piece of it.

149
00:07:45,980 --> 00:07:49,547
You can understand what
kind of tools and pedagogies

150
00:07:49,547 --> 00:07:51,380
and assignments and
technologies might bring

151
00:07:51,380 --> 00:07:56,210
to bear to encourage that to happen
from instructional design perspective.

152
00:07:56,210 --> 00:07:58,595
You can see all the data
and try to get inferences

153
00:07:58,595 --> 00:08:01,220
about what the data are telling
you, about how that is or isn't

154
00:08:01,220 --> 00:08:03,350
working from the data science side.

155
00:08:03,350 --> 00:08:05,570
But at the end of the
day, it turns out that you

156
00:08:05,570 --> 00:08:08,630
provide so many
opportunities to students

157
00:08:08,630 --> 00:08:11,510
that they just choose not to engage in.

158
00:08:11,510 --> 00:08:13,760
But you know that they're
there, they're paying money,

159
00:08:13,760 --> 00:08:14,970
you know that they want to learn.

160
00:08:14,970 --> 00:08:16,850
They want to be earning
decent grades, and they're not

161
00:08:16,850 --> 00:08:19,599
engaging in behaviors that are
clearly in their own self-interest.

162
00:08:19,599 --> 00:08:23,030
So it turns out there's a
behavioral economics piece of it

163
00:08:23,030 --> 00:08:25,340
as well, where there's
some nudging or there's

164
00:08:25,340 --> 00:08:27,032
something that needs to go on there.

165
00:08:27,032 --> 00:08:29,240
So you look at all the things
you have to understand,

166
00:08:29,240 --> 00:08:31,340
and we kind of have a
bulleted list of faculty

167
00:08:31,340 --> 00:08:34,400
that are not learning scientists,
they're not instructional designers,

168
00:08:34,400 --> 00:08:37,280
they're not data scientists,
they're not behavioral economists.

169
00:08:37,280 --> 00:08:41,289
The job of somebody who wants
to make that easy for them

170
00:08:41,289 --> 00:08:46,400
is to bundle all that up in a way
that in kind of one paragraph,

171
00:08:46,400 --> 00:08:49,810
you can explain in natural
language what's happening.

172
00:08:49,810 --> 00:08:52,394
Of course, it's not the full
richness of all the understanding

173
00:08:52,394 --> 00:08:55,351
that you would have had if you went
in to get a master's degree in data

174
00:08:55,351 --> 00:08:58,900
science or something, but enough to
let you leverage tools in powerful ways

175
00:08:58,900 --> 00:09:01,950
to promote and support better learning.

176
00:09:01,950 --> 00:09:06,570
The overwhelming majority of faculty
right now don't have the first--

177
00:09:06,570 --> 00:09:09,180
they haven't had a
single semester course

178
00:09:09,180 --> 00:09:12,540
in just the pedagogy of their
own discipline, or anything.

179
00:09:12,540 --> 00:09:15,306
They did a PhD in physics and
they got a job teaching physics.

180
00:09:15,306 --> 00:09:17,430
And you'll teach the same
way that you were taught,

181
00:09:17,430 --> 00:09:19,489
and that's kind of
the assumption, right?

182
00:09:19,489 --> 00:09:21,780
The idea that if we're not
currently training them just

183
00:09:21,780 --> 00:09:24,184
on the basics of teaching,
that we're somehow

184
00:09:24,184 --> 00:09:26,100
going to succeed in also
training them on data

185
00:09:26,100 --> 00:09:28,849
science and the instructional
design and the other pieces of this,

186
00:09:28,849 --> 00:09:31,760
it seems kind of far fetched to me.

187
00:09:31,760 --> 00:09:35,750
I think our best hope is embodying
those things and tools in ways that

188
00:09:35,750 --> 00:09:39,580
are transparent and usable and open.

189
00:09:39,580 --> 00:09:40,160