The document discusses the implications of learning analytics in education, emphasizing the use of digital tools to visualize student progress and enhance learning outcomes. It covers predictive models and analytics that aim to assist educators in identifying at-risk students and optimizing learning ecosystems. Additionally, it explores various analytics approaches, including social network analysis and discourse analytics, to improve student engagement and participation.

1. University of Technology, Sydney, Dec 2013
Learning Analytics
Should we be concerned about the
digital “quantification” of learning?
Simon Buckingham Shum
Knowledge Media Institute
The Open University UK
simon.buckinghamshum.net
http://linkedin.com/in/simon
@sbskmi #LearningAnalytics
http://

2. aim
leave with a better sense of the
analytics design space
+
better questions
than you can ask right now
2

3. AI &
Argumentation
KMi, OU
Learning
Technology
Semantic Scientific
Publishing
Dialogue / Issue /
Argument Mapping
Learning
Dispositions
Learning
Analytics
Human-Centred
Informatics
Learning
Analytics
+
(Contested)
Collective
Intelligence

4. 70-strong lab prototyping next generation
learning / collaboration / social media
analytics / future internet
4

5. The Hypermedia Discourse Group
Knowledge Media Institute, Open University:
http://kmi.open.ac.uk/projects/member/simon-buckingham-shum
Funders span disciplines, from basic research to applications:
5

6. make the invisible visible
Scholarship
and
Research
make the opaque permeable
make the ephemeral persistent
by using digital tools to craft narrative
around ideas + documents + multimedia
6

7. make the invisible visible
Learning
Analytics
make the opaque permeable
make the ephemeral persistent
by using data about, and generated by
learners, to visualize their progress
7

8. From an analytics product review…
8

9. From an analytics product review…
“Some have tried to argue that
this technology doesn't work out
cost effectively when compared to
conventional tests... but this
misses a huge point. More often
than not, we test after the event
and discover the problem — but
this is too late..”
9

10. Aquarium Analytics!
10

11. 11

12. How is your aquatic ecosystem?
“This means that the keeper can be notified before water
conditions directly harm the fish—an assured outcome of
predictive software that lets you know if it looks like the
pH is due to drop, or the temperature is on its way up.
This way, it’s a real fish saver, as
opposed to a forensic examiner,
post-wipeout.”
(From a review of Seneye, in a hobbyist magazine)
12

13. How is your learning ecosystem?
This means that the teacher can be notified before
learning conditions directly harm the students — an
assured outcome of predictive software that lets you
know if it looks like engagement is due to drop, or
distraction is on its way up.
This way, it’s a real student saver,
as opposed to a forensic
examiner, post-wipeout.
13

14. The rise of analytics…
NMC Horizon 2011 Report:
Learning Analytics
(4-5yrs adoption)
L. Johnson, R. Smith, H. Willis, A. Levine, and K. Haywood, The 2011 Horizon Report (Austin, TX: The New Media Consortium,
2011), http://www.nmc.org/pdf/2011-Horizon-Report.pdf
14

15. The rise of analytics…
Ed-Tech startups explosive growth
Audrey Waters: http://hackeducation.com/2012/11/19/top-ed-tech-trends-of-2012-the-business-of-ed-tech
15

16. The rise of analytics…
LMS/VLEs + Analytics
Publishers + Analytics
16

17. Back to Aquarium Analytics…
17

18. Back to Aquarium Analytics…
fish
aquarium science
learners?
learning science
instructional design
18

19. Purdue University Signals: real time trafficlights for students based on predictive model
19

20. Purdue University Signals: real time trafficlights for students based on predictive model
MODEL:
• 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
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
20

21. Purdue University Signals: real time trafficlights for students based on predictive model
“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.”
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-academicanalytics-promote-student-success
21

22. Predictive analytics @open.edu
Demo-­‐
graphics
VLE
interac)on
Registra)on
Pa.ern
Library
interac)on
CRM
contact
OpenLearn
interac)on
Grades
FutureLearn
interac)on
OU
history
Social
App
X
interac)on
?
How early can we predict
likelihood of dropout, formal
withdrawal, failure?
Now exploring conventional
statistics, machine learning
and growing datasets

23. Predictive analytics @open.edu
Adding in user interaction data from the VLE
Test a range of
predictive models:
final result (pass/fail)
final numerical score
drop in the next TMA
score of the next TMA
Demographics
Previous
results
VLE
activity
A.L. Wolff and Z. Zdrahal (2012). Improving Retention by Identifying and Supporting “At-risk” Students. EDUCAUSE Review Online, JulyAugust 2012. http://www.educause.edu/ero/article/improving-retention-identifying-and-supporting-risk-students

24. Hmmm…
no learning sciences/design
underpinning these predictive models of student success
models based on a mix of
institutional know-how
about student success, and mining
behavioural data
24

25. the opportunity for the
learning sciences
to combine with your university’s
collective
intelligence
25

26. predictive models
are exciting
but there are many other
kinds of analytics
26

27. Analytics 101
Elaborated version of figure from Doug Clow:
h.p://www.slideshare.net/dougclow/the-­‐learning-­‐analy)cs-­‐cycle-­‐closing-­‐the-­‐loop-­‐effec)vely
(slide
5)
ethics
What kinds of learners?
What kinds of learning?
What data could be
generated digitally
from the use context?
What human +/or
software
interventions /
recommendations?
Does your theory
predict patterns
signifying learning?
(you can invent future
technologies if need)
What analytical tools
could be used to find
such patterns?
How to render the analytics,
for whom, and will they
understand them? 27

28. Analytics coming to a VLE near you:
Blackboard basic summary stats
http://www.blackboard.com/Platforms/Analytics/Products/Blackboard-Analytics-for-Learn.aspx
28

29. Student Activity Dashboard (Erik Duval)
Duval E. (2011) Attention please!: learning analytics for visualization and recommendation. Proceedings of the 1st International
Conference on Learning Analytics and Knowledge. Banff, Alberta, Canada: ACM, 9-17.
29

30. Khan Academy has extended great instructional
movies with a tutoring platform with detailed analytics
http://www.youtube.com/watch?v=DLt6mMQH1OY
30

31. Adaptive platforms generate fine-grained
analytics on individuals’ curriculum mastery
https://grockit.com/research
31

32. Intelligent tutoring for skills mastery (CMU)
http://oli.cmu.edu
“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.”
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

33. Track learner activity with a virtual machine
(Abelardo Pardo, LAK13 Conference Keynote)
33
http://www.slideshare.net/abelardo_pardo/bridging-the-middle-space-with-learning-analytics

34. Track learner activity with a virtual machine
(Abelardo Pardo, LAK13 Conference Keynote)
http://www.slideshare.net/abelardo_pardo/bridging-the-middle-space-with-learning-analytics
Calvo, R., O’Rourke, S.T., Jones, J., Yacef, K., Reimann, P., 2011. Collaborative Writing Support Tools on the Cloud. IEEE Transactions on
Learning Technologies, 4(1):88–97
34

35. macro
meso
micro
analytics
35

36. Macro/Meso/Micro Learning Analytics
League Tables
Macro:
region/state/national/international
Data
Interoperability
Initiatives

37. Macro/Meso/Micro Learning Analytics
Macro:
region/state/national/international
Meso:
institution-wide
Univ. Student
info Systems
Business
Intelligence
products to
improve org
processes

38. Macro/Meso/Micro Learning Analytics
Macro:
region/state/national/international
Meso:
institution-wide
Micro:
individual user actions
(and hence cohort)
Learning
Analytics

39. Hard distinctions between Learning +
Academic analytics may dissolve
…as they get joined up, each level enriches the others
Macro:
region/state/national/international
Meso:
institution-wide
Micro:
individual user actions
(and hence cohort)
Aggregation of user traces
enriches meso + macro analytics
with finer-grained process data

40. Hard distinctions between Learning +
Academic analytics may dissolve
…as they get joined up, each level enriches the others
Macro:
region/state/national/international
Meso:
institution-wide
Micro:
individual user actions
(and hence cohort)
Aggregation of user traces
enriches meso + macro analytics
with finer-grained process data
Breadth + depth from macro
+ meso levels add power to
micro analytics

41. Analytics are not the end, but a means
The goal is to optimize the whole system
learners
Intent
curriculum
theories
pedagogies
assessments
tools
feedback
design
outcome
Data
intent
researchers / educators / instructional designers
41

42. Webinar replay on OU analytics strategy
Belinda Tynan & Simon Buckingham Shum (2013). Designing Systemic Learning Analytics at the Open University. SoLAR Open Course, Strategy &
Policy for Systemic Learning Analytics, 11th Oct 2013. http://people.kmi.open.ac.uk/sbs/2013/10/designing-systemic-analytics-at-the-open-university

43. Optimize the system
for what?
43

44. design analytics to achieve
your university’s strategic
goals
(increasingly differentiated as the sector
stratifies?)
44

45. analytics for building learners who handle…
• uncertainty
• novel dilemmas
• conflicting viewpoints
• VUCA life
in C21
US military: Volatile/Uncertain/Complex/Ambiguous
?
45

46. OECD DeSeCo Final Report
Definition & Selection of Key Competencies
“The OECD has collaborated
with a wide range of scholars,
experts and institutions to
identify a small set of key
competencies that help
individuals and whole
societies to meet their goals.”
http://www.deseco.admin.ch
46

47. analytics for
social capital
47

48. Social Network Analysis (SNAPP)
What’s going on
in these discussion forums?
48

49. Social Network Analysis to gain insight into
peer-peer and peer-mentor dynamics
(SNAPP tool)
http://www.slideshare.net/aneeshabakharia/snapp-20minute-presentation
Bakharia A and Dawson S. (2011) SNAPP: a bird's-eye view of temporal participant interaction. Proceedings of
the 1st International Conference on Learning Analytics and Knowledge. Banff, Alberta, Canada: ACM, 168-173.
49

50. Social Network Analysis (SNAPP)
2 learners connect
otherwise separate
clusters
tutor only engaging
with active students,
ignoring disengaged
ones on the edge
50
http://www.slideshare.net/aneeshabakharia/snapp-20minute-presentation

51. Social Learning Analytics about to appear in
products…
http://www.desire2learn.com/products/analytics (this is from a beta demo)
51

52. Visualizing activity in OU Facebook sites
What do students say about their courses, and what are the
patterns of engagement in online communities?

53. discourse analytics for
using language as
a knowledgebuilding tool
53

54. 1st International Workshop on
Discourse-Centric Learning Analytics
solaresearch.org/events/lak/lak13/dcla13
analytics that look
beneath the surface,
and quantify linguistic
proxies for ‘deeper
learning’
http://www.glennsasscer.com/wordpress/wp-content/uploads/2011/10/iceberg.jpg
Beyond number / size / frequency
of posts; ‘hottest thread’

55. Discourse analytics on webinar
textchat
Can we spot the
quality learning
conversations in
a 2.5 hr webinar?
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

56. 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…
Sheffield, UK not as sunny
as yesterday - still warm
See you!
bye for now!
Greetings from Hong Kong
bye, and thank you
Morning from Wiltshire,
80
sunny here!
Bye all for now
60
40
0
-20
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
20
-40
Average Exploratory
-60
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

57. 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…
80
60
40
0
-20
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
20
-40
Average Exploratory
-60
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

58. 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…
Classified as
“exploratory
talk”
(more
substantive
for learning)
100
0
-50
-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
50
“nonexploratory”
Averag
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

59. Discourse analytics on webinar
textchat
Visualizing by individual user. The gradient of the threshold line is
adjusted to every 5 posts in 6 classified as “Exploratory Talk”
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

60. “Rhetorical parsing” to identify constructions
signifying scholarly writing
OPEN QUESTION:
“… little is known …”
“… role … has been elusive”
“Current data is insufficient …”
SURPRISE:
“We have recently observed ...
surprisingly”
“We have identified ... unusual”
“The recent discovery ... suggests
intriguing roles”
CONTRASTING IDEAS:
“… unorthodox view resolves …”
“In contrast with previous
hypotheses ...”
“... inconsistent with past
findings ...”
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
Simsek D, Buckingham Shum S, Sándor Á, De Liddo A and Ferguson R. (2013) XIP Dashboard: http://oro.open.ac.uk/37391

61. “What are the key contributions of this text?
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
Simsek D, Buckingham Shum S, Sándor Á, De Liddo A and Ferguson R. (2013) XIP Dashboard: http://oro.open.ac.uk/37391

62. Social Learning Analytics
• Explosive growth in social
media
• The open/free content
paradigm
• Evidence of a global shift in
societal attitudes which
increasingly values
participation
• Innovation depends on
reciprocal social
relationships, tacit knowing
Buckingham Shum, Simon and Ferguson, Rebecca (2012). Social Learning Analytics. Journal of Educational Technology and Society, 15(3) pp. 3–26.
http://oro.open.ac.uk/34092

63. intrinsic motivation
self-regulation
resilience
63

64. Why do dispositions matter?
“Knowledge of methods alone
will not suffice: there must
be the desire, the will,
to employ them. This desire is
an affair of personal
disposition.”
John Dewey
Dewey, J. How We Think: A Restatement of the Relation of Reflective Thinking to the Educative
Process. Heath and Co, Boston, 1933
64

65. Why do dispositions matter?
“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
65

66. Why do dispositions matter?
“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
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
66

67. Why do dispositions matter?
“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.
The purpose of learning in the 21st century
is not to recite inert knowledge but to
transform it.
It’s time to change the subject.”
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
67

68. How can we model and
learning
dispositions in order
quantify
to develop analytics?
68

69. Dispositional
Learning
Analytics
Workshop
http://learningemergence.net/events/lasi-dla-wkshp
69

70. Validated as loading onto
7 dimensions of “Learning Power”
Ruth Deakin Crick
Grad. School of Education
Being Stuck & Static
Changing & Learning
Data Accumulation
Meaning Making
Passivity
Critical Curiosity
Being Rule Bound
Isolation & Dependence
Being Robotic
Fragility & Dependence
Creativity
Learning Relationships
Strategic Awareness
Resilience

71. Analytics for lifelong/lifewide
learning dispositions: ELLI
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

72. Learning to Learn: 7 Dimensions of Learning Power
Factor analysis of the literature plus expert interviews: identified seven
dimensions of effective “learning power”, since validated empirically with
learners at many levels. (Deakin Crick, Broadfoot and Claxton, 2004)

73. Learning to Learn: 7 Dimensions of Learning Power
73

74. next step: platforms for
Dispositional Learning
Analytics
DLA Workshop: http://learningemergence.net/events/lasi-dla-wkshp
74

75. Primary School EnquiryBloggers
Bushfield School, Wolverton, UK
EnquiryBlogger: blogging for Learning Power & Authentic Enquiry
http://learningemergence.net/2012/06/20/enquiryblogger-for-learning-power-authentic-enquiry

76. 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

77. EnquiryBlogger
dashboard – direct
navigation to learner’s
blogs from the visual
analytic

78. Could a platform generate an
ELLI profile from user traces?
Questioning and
challenging may
load onto Critical
Curiosity
Sharing relevant
resources from
other contexts
may load onto
Meaning Making
Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn
http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium
Different social
network patterns
in different
contexts may
load onto
Learning
Relationships
Repeated
attempts to pass
an online test
may load onto
Resilience

79. Envisioning a social learning analytics
dashboard
1
5
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.!
2
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.
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
3
4

80. towards wholistic
learning analytics
Ruth Deakin Crick, Howard Green, Steven Barr

81. Towards wholistic analytics on the
health of a learning community
Academic results are important but less easily
quantifiable measures of success are vital to
many institutions’ vision to nurture
• life-long/life-wide learners
• employability skills • citizenship
• self-confidence • teamwork
• emotional wellbeing…
81

82. Hierarchical Process Modelling
(Univ. Bristol PeriMeta tool)
Seeing a learning community as a complex adaptive
system requires the voices of learners, teachers,
leaders, parents and ‘external’ stakeholders
82

83. Hierarchical Process Modelling
(Univ. Bristol PeriMeta tool)
The core mission and values of
a network of school academies
83

84. Hierarchical Process Modelling
(Univ. Bristol PeriMeta tool)
Hierarchical Process
Modelling transforms
qualitative and quantitative
inputs into a multi-level visual
analytic
84

85. ‘Italian Flag’ visual analytic
Degree of green / white / red reflects current
certainty over availability of evidence
supporting / unknown / challenging
85

86. ‘Italian Flag’ model
Converting learner/leader/teachers’ linguistic survey
ratings and confidence into quantitative values
86

87. ‘Italian Flag’ visual analytic
Degree of green / white / red reflects current
certainty over availability of evidence
supporting / unknown / challenging
87

88. thorny issues
88

89. learning analytics
are
not neutral
89

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

91. cf. Bowker and Starr’s “Sorting Things Out”
on classification schemes
“A marker of the health of the
learning analytics field will be
the quality of debate around
what the technology renders
visible and leaves invisible.”
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, 2012, Vancouver, BC). ACM. Eprint: http://oro.open.ac.uk/32823

92. Analytics are infused with human values
Data does not ‘speak for itself’
ethics
What kinds of learners?
What kinds of learning?
What data could be
generated digitally
from the use context?
What human +/or
software
interventions /
recommendations?
Does your theory
predict patterns
signifying learning?
(you can invent future
technologies if need)
What analytical tools
could be used to find
such patterns?
How to render the analytics,
for whom, and will they
understand them? 92

93. The Wal-Martification of education?
“data narrowness”
“instrumental learning”
“students with no curiosity”
“The basic question is not
what can we measure?
The basic question is
what does a good
education look like?
Big questions.
http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college
http://lak12.wikispaces.com/Recordings
93

94. Will staff know how to
read and write analytics?
This will become a key literacy.
94

95. Learning technology is not neutral
Any technology embodies
epistemological,
pedagogical and
assessment assumptions
epistemology
assessment
pedagogy
Knight S, Buckingham Shum S and Littleton K. (2013) Epistemology, Pedagogy, Assessment and Learning Analytics. Proc. 3rd
International Conference on Learning Analytics & Knowledge. Leuven, BE: ACM, 75-84 Open Access Eprint: http://oro.open.ac.uk/36635
95

96. Algorithms are not neutral
http://governingalgorithms.org
Algorithms increasingly
shape our lives: we need to
go in eyes wide open, and
encourage critical debate 96

97. join the learning
analytics global
community
97

98. Join the community…
http://SoLAResearch.org
replays of all
previous
conference
presentations
http://LAKconference.org
98

99. Join the community…
replays of all
sessions
http://www.solaresearch.org/events/lasi
99

100. JISC Briefings on Learning Analytics
http://publications.cetis.ac.uk/c/analytics
100

101. EDUCAUSE Briefings on Learning Analytics
http://www.educause.edu/library/learning-analytics
101

102. Learning Analytics Policy Brief
(UNESCO • IITE)
http://bit.ly/LearningAnalytics
102

103. Systems leadership and learning:
LearningEmergence.net

104. what this
all means
104

105. The big shifts that analytics could bring…
Organisational
Culture
Academic
Culture
evidence-based
decisions and
org learning
data-intensive
learning sciences/
educ research
Practitioner
Culture
C21
Qualities
evidence impact of
learning designs;
timely interventions
place these on a firm
empirical evidence
base
105

106. The new research+practice vista…
data-culture
dynamics
how do HEIs manage
the embedding of
real time analytics
services?
educator data
literacy
how do staff learn to
read and write
analytics?
sensemaking
meets
computation
creative intelligence +
computational
thinking
pedagogical
innovation
how do learning
analytics change
student experience?
106