Keynote address, EdMedia 2014, 25th June, Tampere, Finland. Movie replay: http://people.kmi.open.ac.uk/sbs/2014/06/edmedia2014-keynote

1. Learning Analytics:
Welcome to the future of assessment?
Simon Buckingham Shum
Knowledge Media Institute, The Open University
Visiting Fellow, University of Bristol
(From August, University of Technology Sydney)
simon.buckinghamshum.net
twitter @sbskmi #LearningAnalytics #edmedia See the question at #edmediakeynote
Keynote
address,
EdMedia
2014,
25th
June,
Tampere,
Finland
1

2. learning objective: leave with
an expanded vision of analytics
better questions to ask in your next
analytics conversation
2

3. Big Data status report:
3
“Big data is like teenage sex: everyone
talks about it, nobody really knows
how to do it, everyone thinks
everyone else is doing it, so everyone
claims they are doing it...”
https://www.facebook.com/dan.ariely/posts/904383595868

4. When the Chancellor announces the adoption
of a new economic modelling technique…
4
…we query the
limitations
of the model

5. 5
…we query the
limitations
of the model

6. Siri is smart…
6
I was speaking at this event:
http://codeactsineducation.wordpress.com

7. Siri is smart…
“Find code acts in
education”
7

8. Siri is smart…
“Find code acts in
education”
8

9. Similarly, when we are confronted with
new learning analytics…
LAK13 Panel: Educational Data Scientists: A Scarce Breed
http://people.kmi.open.ac.uk/sbs/2013/03/lak13-edu-data-scientists-scarce-breed
John Behrens
(Pearson)
9

10. LAK13 Panel: Educational Data Scientists: A Scarce Breed
http://people.kmi.open.ac.uk/sbs/2013/03/lak13-edu-data-scientists-scarce-breed
John Behrens
(Pearson)
10
…we should query the limitations of the model

11. h=ps://twi=er.com/Wiswijzer2/status/414055472451575808
“Note:
check
the
huge
difference
between
knowing
and
measuring…”
11

12. a
few
quick
examples
of
learning
analy5cs
12

13. It’s out of the labs and into products: every learning
tool now has an “analytics dashboard” (a Google image search)
13

14. Intelligent tutoring for skills mastery (CMU)
Lovett M, Meyer O and Thille C. (2008) The Open Learning Initiative: Measuring the effectiveness of the OLI statistics course in accelerating student
learning. Journal of Interactive Media in Education 14. http://jime.open.ac.uk/article/2008-14/352
“In this study, results showed that
OLI-Statistics students [blended
learning] learned a full semester’s
worth of material in half as much
time and performed as well or
better than students learning from
traditional instruction over a full
semester.”

15. Purdue University Signals: real time traffic-lights for
students based on predictive model
15
Campbell et al (2007). Academic Analytics: A New Tool for a New Era, EDUCAUSE
Review, vol. 42, no. 4 (July/August 2007): 40–57. http://bit.ly/lmxG2x
Validate a statistical model from:
• ACT or SAT score
• Overall grade-point average
• CMS usage composite
• CMS assessment composite
• CMS assignment composite
• CMS calendar composite
Predicted 66%-80% of struggling
students who needed help

16. Purdue University Signals: real time traffic-lights for
students based on predictive model
16
Pistilli, M. D., Arnold, K. and Bethune, M., Signals: Using Academic Analytics to
Promote Student Success. EDUCAUSE Review Online, July/Aug., (2012).
http://www.educause.edu/ero/article/signals-using-academic-analytics-
promote-student-success
“Results thus far show that students
who have engaged with Course Signals
have higher average grades and seek
out help resources at a higher rate
than other students.”

17. Spatial clustering algorithm to provoke reflection
17

18. Posture analysis of fieldwork students
18

19. …and many more examples including
discourse analytics language technologies to assess the quality
of online postings and debate
social network analytics graph analytics to assess strength
and topics of interpersonal ties
epistemic game analytics assessing the degree of
professional engagement in authentic project scenarios
visualizations to reveal important patterns of tool use over time
(see other presentations and tutorials)
19

20. but
before
we
get
carried
away,
let’s
just
pause…
20

21. Selwyn, N. (2014). Data entry: towards the critical study of digital data and education. Learning, Media and Technology. http://dx.doi.org/
10.1080/17439884.2014.921628
“observing, measuring, describing,
categorising, classifying, sorting, ordering
and ranking). […] these processes of meaning-making are never
wholly neutral, objective and ‘automated’ but are fraught with
problems and compromises, biases and
omissions.
21

22. For Morozov, analytics is
where technological
solutionism hits education:
22
“This flight from thinking
and the urge to replace
human judgments with
timeless truths produced by
algorithms is the underlying
driving force of
solutionism.”

23. Could analytics help us shift from the calculating
mind to the contemplative mind?
23
See also:
Complexity, Computing, Contemplation, Learning?
http://learningemergence.net/2011/05/04/cccl
http://www.contemplativecomputing.org/2011/03/first-draft-of-a-contemplative-computing-article.html
Alex Pang: “A contemplative stance can help people be
more creative; deal with complex problems that
require months or years to solve […]
Contemplation promotes both self-sufficiency and
close, questioning observation of the world, and both
are particularly valuable in this moment in the history
of technology.”
Calculating Mind, Contemplative Mind
http://people.kmi.open.ac.uk/sbs/2008/09/calculating-contemplative-mind

24. unpacking
the
core
ques5on
for
learning
analy5cs
24

25. can
we
tell
from
your
digital
profile
if
you’re
learning?
25

26. can
we
tell
from
your
digital
profile
if
you’re
learning?
26
Who?

27. can
we
tell
from
your
digital
profile
if
you’re
learning?
27
Who?
How? With what confidence?
After what kinds of training?

28. can
we
tell
from
your
digital
profile
if
you’re
learning?
28
Who?
How? With what confidence?
After what kinds of training?
Sourcing which data,
with what integrity?

29. can
we
tell
from
your
digital
profile
if
you’re
learning?
29
Who?
How? With what confidence?
After what kinds of training?
Sourcing which data,
with what integrity?
What kind of learning?
What kind of learner?

30. Accounting tools are not neutral
Du Gay, P. and Pryke, M. (2002) Cultural Economy: Cultural Analysis and Commercial Life. Sage, London. pp. 12-13
“accounting tools...do not simply
aid the measurement of economic
activity, they shape the
reality they measure”

31. In
what
senses
do
analy5cs
“shape
the
reality
they
measure”?
31

32. How
do
analyQcs
shape
educaQon?
Analytics reports at the
organisational and national
levels come with
consequences at different
scales — sometimes punitive,
often impacting millions of
people.
PoliQcally
32

33. How
do
analyQcs
shape
educaQon?
What data, concepts
and relationships do
the analytics
designers seek to
model?
Ontologically
33

34. Bowker, G. C. and Star, L. S. (1999). Sorting Things Out: Classification and Its Consequences. MIT Press, Cambridge, MA, pp. 277, 278, 281
“Classification systems provide both a
warrant and a tool for forgetting [...]
what to forget and how to forget it [...]
The argument comes down to asking not
only what gets coded in but what gets
coded out of a given scheme.”
34

35. Visualising “attainment” and “progress”
35

36. Which analytics could reflect the progress that ‘Joe’
has made on so many other fronts other than his SATS?
36

37. Key modelling issue: unit of analysis
! Discourse analysis: how do machines and humans differ in the
way they segment a transcript to make sense of it?
! Rosé, C. P., & Tovares A. (in press). What Sociolinguistics and Machine Learning Have to Say to One Another about Interaction
Analysis. In L. Resnick, Asterhan C., & Clarke S. (Eds.), Socializing Intelligence Through Academic Talk and Dialogue. Washington,
D.C.: American Educational Research Association
! Collective intelligence: If we are shifting from a sole focus on
individual accomplishment, to that of group knowledge
construction and performance, how do analytics assess
changes in a group’s knowledge and processes?
! Chen, B., & Resendes, M. (2014). Uncovering what matters: Analyzing transitional relations among contribution types in
knowledge-building discourse. In Proceedins of the Fourth International Conference on Learning Analytics And
Knowledge - LAK ’14 (pp. 226–230). New York, New York, USA: ACM Press. doi:10.1145/2567574.2567606
37

38. How
do
analyQcs
shape
educaQon?
What thresholds, samples,
relationships, patterns, etc. do
the algorithms encode and
seek?
On what basis is a
recommendation engine
proposing interventions?
Algorithmically
38

39. governingalgorithms.org
Learning
AnalyQcs
In
an
increasingly
algorithmic
world
[…]
What,
then,
do
we
talk
about
when
we
talk
about
“governing
algorithms”?
39

40. governingalgorithms.org
A
technology
or
an
epistemology?
Barocas,
S.,
Hood,
S.
and
Ziewitz,
M.
(2013).
Governing
Algorithms:
A
Provoca5on
Piece.
Social
Science
Research
Network
Paper
2245322.
DOI:
h=p://dx.doi.org/10.2139/ssrn.2245322
Secrecy,
obscurity,
inscrutability
Agency,
automaQon,
accountabiliQes
A
typology
of
algorithms
by
genre?
The
inscrutability
of
algorithms
NormaQvity,
bias,
values
40

41. Open Learning Analytics: open source
algorithmic transparency (at least for those who are literate)
no analytics ‘lock-in’ for educators
http://www.solaresearch.org/mission/ola

42. How
do
analyQcs
shape
educaQon?
What meaning-making does
the representation and
interaction design
encourage?
SemioQcally
42

43. outcome
How
do
analyQcs
shape
educaQon?
By
changing
the
system
dynamics
researchers
/
educators
/
instrucQonal
designers
administrators
/
leaders
/
policymakers
intent
43

44. outcome
How
do
analyQcs
shape
educaQon?
By
changing
the
system
dynamics
Faster
feedback
loops
could
enable
more
rapid
adaptaQon:
of
agents’
behaviour,
and
of
learning
resources
and
designs
researchers
/
educators
/
instrucQonal
designers
administrators
/
leaders
/
policymakers
intent
44

45. DelegaQon
of
authority
to
define
goals,
analyQcs,
and
meaning
How
do
analyQcs
shape
educaQon?
Distribution of power
between educators,
learners, leaders,
community…?
?
?

46. How
do
analyQcs
shape
educaQon?
epistemology
pedagogyassessment
Knight, S., Buckingham Shum, S. and Littleton, K. (In Press, 2014). Epistemology, Assessment, Pedagogy: Where Learning
Meets Analytics in the Middle Space. Journal of Learning Analytics. Open Access Eprint: http://oro.open.ac.uk/39226
the
middle
space of
learning analytics
What epistemological
assumptions are shaping
the assessment regime,
and hence the
pedagogy? What
questions are analytics
used to help answer?
46

47. Example: epistemological assumptions
47
Knight, S., Buckingham Shum, S. and Littleton, K. (In Press, 2014). Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the
Middle Space. Journal of Learning Analytics. Open Access Eprint: http://oro.open.ac.uk/39226
Allows testing of problem-solving
and analysis - sifting information
"if you allow communication,
discussions, searches and so on, you
eliminate cheating because it's not
cheating any more. That is the way
we should think."

48. Figure
from
Doug
Clow:
h=p://www.slideshare.net/dougclow/the-­‐learning-­‐analyQcs-­‐cycle-­‐closing-­‐the-­‐loop-­‐effecQvely
(slide
5)
How
do
analyQcs
shape
educaQon?
All
of
the
above
are
encapsulated
in
any
learning
analyQcs
deployment
48

49. 49
What
kinds
of
learners?
What
kinds
of
learning?
What
data
could
be
generated
digitally
from
the
use
context?
How
is
it
‘cleaned’?
Does
your
theory
predict
pa=erns
signifying
learning?
What
human
+/or
solware
intervenQons
/
recommendaQons?
How
to
render
the
analyQcs,
for
whom,
and
will
they
understand
them?
What
analyQcal
tools
could
be
used
to
find
such
pa=erns?
How
do
analyQcs
shape
educaQon?

50. Conclusion:
AnalyQcs
profoundly
shape
educaQon…
Ontologically
Algorithmically
SemioQcally
Systemically
PoliQcally
Authority?
50

51. what
kinds
of
learning
are
we
opQmising
the
system
for?
51

52. Learning analytics for this?
“The test of successful education is
not the amount of knowledge that
pupils take away from school, but
their appetite to know and
their capacity to learn.”
Sir Richard Livingstone, 1941
52

53. “We’re looking at the profiles of what it means
to be effective in the 21st century. […]
Resilience will be the defining concept.
When challenged and bent, you learn and
bounce back stronger.”
“Dispositions are now at least as
important as Knowledge and Skills. …
They cannot be taught.
They can only be cultivated.”
John Seely Brown
53
US Dept. of Educ. http://reimaginingeducation.org conference (May 28, 2013)
Dispositions clip: http://www.c-spanvideo.org/clip/4457327
Whole talk: http://www.c-spanvideo.org/program/SecD
Learning analytics for this?

54. “It’s more than knowledge and skills. For the
innovation economy, dispositions come
into play: readiness to
collaborate; attention to
multiple perspectives; initiative;
persistence; curiosity.”
Larry Rosenstock
LearningREimagined project: http://learning-reimagined.com
Larry Rosenstock:
http://audioboo.fm/boos/1669375-50-seconds-of-larry-rosenstock-ceo-of-hightechhigh-on-how-he-would-re-imagine-learning
Learning analytics for this?

55. “In the growth mindset, people believe that
their talents and abilities can be developed
through passion, education, and persistence
…
It’s about a commitment to … taking
informed risks … surrounding
yourself with people who will
challenge you to grow”
Carol Dweck
Interview with Carol Dweck:
http://interviewscoertvisser.blogspot.co.uk/2007/11/interview-with-carol-dweck_4897.html
Another interview: http://www.youtube.com/watch?v=ICILzbB1Obg
Learning analytics for this?

56. Important work by Tony Bryk et al.:
Drivers of “Productive Persistence”
http://www.carnegiealphalabs.org/persistence/

57. Important work by Tony Bryk et al.:
Drivers of “Productive Persistence”
http://www.carnegiealphalabs.org/persistence/
Note: a research-
based rationale
for architecting a
suite of analytics
techniques

58. Bryk: “sense of belonging” a key
predictor of remedial maths completion
58
http://learningemergence.net/2014/05/27/tony-bryk-lecture

59. Envisioning a wholistic university education
(and analytics to match)
59
http://reinventors.net/series/reinvent-university

60. discourse
learning
analy5cs?
60

61. 1st International Workshop on
Discourse-Centric Learning Analytics
analytics that look beneath
the surface, and quantify
linguistic proxies for ‘deeper
learning’
Beyond number / size / frequency
of posts; ‘hottest thread’
http://www.glennsasscer.com/wordpress/wp-content/uploads/2011/10/iceberg.jpg
solaresearch.org/events/lak/lak13/dcla13

62. Discourse analytics on webinar textchat
Ferguson, R. and Buckingham Shum, S., Learning analytics to identify exploratory dialogue within synchronous text chat. In: 1st
International Conference on Learning Analytics and Knowledge (Banff, Canada, 2011). ACM
Can we spot the
quality learning
conversations in a
2.5 hr webinar?

63. -60
-40
-20
0
20
40
60
80
9:28
9:32
9:36
9:40
9:41
9:46
9:50
9:53
9:56
10:00
10:05
10:07
10:07
10:09
10:13
10:17
10:23
10:27
10:31
10:35
10:40
10:45
10:52
10:55
11:04
11:08
11:11
11:17
11:20
11:24
11:26
11:28
11:31
11:32
11:35
11:36
11:38
11:39
11:41
11:44
11:46
11:48
11:52
11:54
12:00
12:03
12:04
12:05
Average Exploratory
Discourse analytics on webinar textchat
Sheffield, UK not as sunny
as yesterday - still warm
Greetings from Hong Kong
Morning from Wiltshire,
sunny here!
See you!
bye for now!
bye, and thank you
Bye all for now
Given a 2.5 hour webinar, where in the live
textchat were the most effective learning
conversations?
Not at the start and end of a webinar…
Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S., An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. In: Proc.
3rd International Conference on Learning Analytics & Knowledge (Leuven, BE, 8-12 April, 2013). ACM. http://oro.open.ac.uk/36664

64. -60
-40
-20
0
20
40
60
80
9:28
9:32
9:36
9:40
9:41
9:46
9:50
9:53
9:56
10:00
10:05
10:07
10:07
10:09
10:13
10:17
10:23
10:27
10:31
10:35
10:40
10:45
10:52
10:55
11:04
11:08
11:11
11:17
11:20
11:24
11:26
11:28
11:31
11:32
11:35
11:36
11:38
11:39
11:41
11:44
11:46
11:48
11:52
11:54
12:00
12:03
12:04
12:05
Average Exploratory
Discourse analytics on webinar textchat
Given a 2.5 hour webinar, where in the live
textchat were the most effective learning
conversations?
Not at the start and end of a webinar
but if we zoom in on a peak…
Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S., An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. In: Proc.
3rd International Conference on Learning Analytics & Knowledge (Leuven, BE, 8-12 April, 2013). ACM. http://oro.open.ac.uk/36664

65. Discourse analytics on webinar textchat
-100
0
100
9:28
9:40
9:50
10:00
10:07
10:17
10:31
10:45
11:04
11:17
11:26
11:32
11:38
11:44
11:52
12:03
Averag
Classified as
“exploratory
talk”
(more
substantive
for learning)
“non-
exploratory”
Given a 2.5 hour webinar, where in the live
textchat were the most effective learning
conversations?
Not at the start and end of a webinar
but if we zoom in on a peak…
Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S., An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. In: Proc.
3rd International Conference on Learning Analytics & Knowledge (Leuven, BE, 8-12 April, 2013). ACM. http://oro.open.ac.uk/36664

66. Rhetorical discourse analytics
66
OPEN QUESTION:
“… little is known …”
“… role … has been elusive”
“Current data is insufficient …”
CONTRASTING IDEAS:
“… unorthodox view resolves …”
“In contrast with previous
hypotheses ...”
“... inconsistent with past
findings ...”
SURPRISE:
“We have recently observed ... surprisingly”
“We have identified ... unusual”
“The recent discovery ... suggests intriguing
roles”
http://technologies.kmi.open.ac.uk/cohere/2012/01/09/cohere-plus-automated-rhetorical-annotation
De Liddo, A., Sándor, Á. and Buckingham Shum, S., Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation
Study. Computer Supported Cooperative Work, 21, 4-5, (2012), 417-448. http://oro.open.ac.uk/31052

67. Rhetorical discourse analytics
67
Human analyst

68. Rhetorical discourse analytics
68
Human analyst Computational analyst
http://technologies.kmi.open.ac.uk/cohere/2012/01/09/cohere-plus-automated-rhetorical-annotation
De Liddo, A., Sándor, Á. and Buckingham Shum, S., Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation
Study. Computer Supported Cooperative Work, 21, 4-5, (2012), 417-448. http://oro.open.ac.uk/31052

69. Rhetorical discourse analytics
69
Duygu Simsek’s PhD: http://people.kmi.open.ac.uk/simsek/research/
Glimpses of analytics capable of
detecting higher order thinking.
But humans will always read
differently to machines
Can we correlate this with
“academic writing”, and can such
analytics be used as formative
feedback on drafts?

70. Rhetorical discourse analytics
70
Simsek D, Buckingham Shum S, Sándor Á, De Liddo A and Ferguson R. (2013) XIP Dashboard: http://oro.open.ac.uk/37391
CONTRAST
SUMMARY &
CONTRIBUTION

71. disposi5onal
learning
analy5cs?
71

72. Dispositional
Learning
Analytics
Workshop
72
http://learningemergence.net/events/lasi-dla-wkshp
http://learningemergence.net/2014/03/01/
assessing-learning-dispositions-academic-mindsets

73. Observation
informal and formal
Self-Diagnostic
informal and formal
Behavioural
Analytics
Assessing Learning Dispositions/Mindsets
Future sweetspot...
multiple lenses to
provoke self-reflection

74. Childrens’ informal self-assessment of
“managing distractions”
http://learningemergence.net/2014/03/01/assessing-learning-dispositions-academic-mindsets

75. Mindset
Works
mindsetworks.com
Based on the educational
research of Carol Dweck
into growth mindsets
(Stanford)
75

76. ELLI: Effective Lifelong Learning Inventory

77. Quantifying learning dispositions
agency; identity; motivation; responsibility
Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics.
Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, Vancouver). Eprint: http://oro.open.ac.uk/32823
http://learningemergence.net/2012/04/30/learning-powered-learning-analytics
A
wholisQc
visual,
intended
to
build
intrinsic
moQvaQon,
inviQng
stretch,
providing
a
new
language,
provoking
conversaQon
that
Qes
to
the
learner’s
idenQty

78. Self-report through reflective blogging
9-10 yr old EnquiryBloggers • Bushfield School, Wolverton, UK
EnquiryBlogger Wordpress Multisite plugins
http://learningemergence.net/tools/enquiryblogger
78

79. Masters level EnquiryBloggers
Graduate School of Education, University of Bristol
EnquiryBlogger: blogging for Learning Power & Authentic Enquiry
http://learningemergence.net/2012/06/20/enquiryblogger-for-learning-power-authentic-enquiry
79

80. 80
EnquiryBlogger teacher’s
dashboard – direct navigation to
learners’ blogs from the visual
analytic

81. http://learningemergence.net/2014/03/01/assessing-learning-dispositions-academic-mindsets
2020? personal data cloud generates my dispositional
profile for reflection from behavioural data?
>>> help me take responsibility for my own learning
Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn
http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium
Simon Knight: http://people.kmi.open.ac.uk/knight/2014/02/knowledge-in-search
Social network patterns,
teamwork effectiveness
and initiation of
relationships
Questioning, arguing
and search behaviours
reveal intrinsic curiosity
and epistemic
commitments
Tagging/sharing/
blogging/social patterns
reveal how you see
connections between
ideas
Behavioural and somatic
traces associated with
perseverance, grit,
tenacity; overcoming
panic/stress when
stretched

82. Your most
recent mood
comment:
“Great, at last
I have found all
the resources
that I have
been looking
for, thanks to
Steve and
Ellen.
In your last discussion with your mentor, you decided
to work on your resilience by taking on more
learning challenges
Your ELLI Spider
shows that you have
made a start on
working on your
resilience, and that
you are also
beginning to work on
your creativity, which
you identified as
another area to work
on.
1 2 3
45
Envisioning a social learning analytics dashboard
Ferguson R and Buckingham Shum S. (2012) Social Learning Analytics: Five Approaches. Proc. 2nd International Conference on Learning Analytics &
Knowledge. Vancouver, 29 Apr-2 May: ACM: New York, 23-33. DOI: http://dx.doi.org/10.1145/2330601.2330616 Eprint: http://oro.open.ac.uk/32910
82

83. Systems leadership and learning:
LearningEmergence.net
83

84. looking forward…

85. 85
The big shifts that analytics could bring…
Organisational
Culture
evidence-based
decisions and
org learning
Academic
Culture
data-intensive
learning sciences/
educ research
Practitioner
Culture
evidence impact of
learning designs;
timely interventions
C21
Qualities
place these on a firm
empirical evidence
base

86. 86
Critical zones for research+practice…
data-culture
org. learning
how do HEIs manage
the embedding of
real time analytics
services?
sensemaking
meets
computation
creative intelligence +
computational
thinking
educator data
literacy
how do staff learn to
read and write
analytics?
pedagogical
innovation
how do learning
analytics change
student experience?

87. how to join in
87

88. The professional society…
88
http://SoLAResearch.org

89. Next Week…
LASI #lasi2014
Learning Analytics Summer Institute
International Society
Educational
Data Mining"
89

90. h=p://www.solaresearch.org/events/lasi-­‐2/lasi2014/lasi-­‐local
90

91. The Learning Analytics Conference
March 16-20, 2015 (NY)
91

92. conclusion
analy5cs
will
shape
educa5on
—
on
mul5ple
dimensions
an
analy5cs
approach
perpetuates
an
educa5onal
worldview
—
so
let’s
ensure
this
is
inten5onal
–
not
accidental...