The document discusses the emerging field of learning analytics, highlighting its transformative potential in education by predicting and improving student engagement and performance. It outlines various predictive models and analytics tools being developed, such as Purdue University's Signals, which uses real-time data to identify at-risk students and enhance their learning outcomes. Furthermore, it explores the intersection of learning analytics with social media and discourse analytics, advocating for a data-driven approach to educational practices.

1. Learning Analytics
The New Burden of Knowledge
Simon Buckingham Shum
Knowledge Media Institute
The Open University UK
http://simon.buckinghamshum.net
http://linkedin.com/in/simon
INSIGHT Centre for Data Analytics, Univ. Galway, 2 Oct 2013
http://www.insight-centre.org
@sbskmi #LearningAnalytics

2. mission
walk out with
better questions
than you can ask right now about analytics
new tech and collaboration opportunities
to advance
education 2

3. why are we seeing this?...
3

4. Why are we seeing this?...
4
VLEs + Analytics Publishers + Analytics

5. 5
Audrey Waters: http://hackeducation.com/2012/11/19/top-ed-tech-trends-of-2012-the-business-of-ed-tech
Ed-Tech startups explosive growth
Why are we seeing this?...

6. 6https://www.edx.org/about
“this is big data, giving
us the chance to ask big
questions about
learning”
Why are we seeing this?...

7. 7http://careers.stackoverflow.com/jobs/35348/software-engineer-analytics-coursera
Why are we seeing this?...

8. 8
Why are we seeing this?...
http://www.independent.ie/lifestyle/education/trinity-joins-elite-colleges-to-offer-free-online-courses-29355438.html

9. the data/analytics
tsunami is about to hit
the education sector
9

10. Data and analytics are transforming
business, government and public services
10
Why would Higher Education be immune?
Why wouldn’t a sector focused on evidence-based
thinking and action welcome it?
A critical discussion is emerging
More later…

11. 11L. 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
NMC Horizon 2011 Report:
Learning Analytics
(4-5yrs adoption)
Analytics is being heralded…
(2013 report)

12. 12
Continuous coverage…
http://www.online-educa.com/OEB_Newsportal/whats-so-big-about-big-data

13. 13
…and debated…
http://www.online-educa.com/OEB_Newsportal/we-urgently-need-to-safeguard-free-will-in-the-age-of-big-data

14. 14

15. 15

16. Tectonic forces are reshaping the
learning landscape…
16

17. the opportunity for
learning design
learning sciences
17

18. From an analytics product review…
18

19. 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..”
19

20. Aquarium Analytics!
20

21. 21

22. 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)
22

23. 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.
23

24. 24
Back to Aquarium Analytics…

25. 25
fish
aquarium science
learners?
learning science
instructional design
Back to Aquarium Analytics…

26. Purdue University Signals: real time traffic-
lights for students based on predictive model
26

27. Purdue University Signals: real time traffic-
lights for students based on predictive model
27
Predicted 66%-80%
of struggling
students who
needed help
MODEL:
• ACT or SAT score
• Overall grade-point average
• CMS usage composite
• CMS assessment composite
• CMS assignment composite
• CMS calendar composite
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

28. Purdue University Signals: real time traffic-
lights for students based on predictive model
28
“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-academic-
analytics-promote-student-success

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

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

31. the opportunity for the
learning sciences
to combine with your university’s
collective
intelligence
31

32. macro
meso
micro
analytics
32

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

34. Macro/Meso/Micro Learning Analytics
Meso:
institution-wide
Macro:
region/state/national/international
Business
Intelligence
Products

35. Business Intelligence
≠
Learning Analytics

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

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

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

39. Insight Centre
intersection with
learning analytics?
39

40. Insight Centre R&D
40http://www.insight-centre.org
There is active research (and
often product development) at
the intersection of education
and all of these tech R&D
challenges

41. Insight Centre R&D could tackle education
41
http://adenu.ia.uned.es/workshops/recsystel2010 www.educationaldatamining.org
http://linkedup-project.eu/2013/03/17/using-linked-data-in-learning-analytics-a-tutorial-by-the-linkedup-consortium
http://www.slideshare.net/erik.duval/20130703-lasi-stanforderik
http://www.dia.uniroma3.it/~umap2013/

42. predictive models
are exciting
but there are many other
kinds of analytics
42

43. Analytics coming to a VLE near you:
e.g. Blackboard
43
http://www.blackboard.com/platforms/analytics/overview.aspx
http://www.blackboard.com/Platforms/Analytics/Products/Blackboard-Analytics-for-Learn.aspx

44. 44
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.

45. 45
Khan Academy
http://www.youtube.com/watch?v=DLt6mMQH1OY
Khan Academy has extended great instructional
movies with a tutoring platform with detailed analytics

46. 46
https://grockit.com/research
Adaptive platforms generate fine-grained
analytics on curriculum mastery

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

48. 48
Are students using the right tools at the right
time in the right way? (Abelardo Pardo, LAK13 Keynote)
http://www.slideshare.net/abelardo_pardo/bridging-the-middle-space-with-learning-analytics

49. Social Learning Analytics
Buckingham Shum, Sand Ferguson, R (2012). Social Learning Analytics. Journal of Educational Technology and Society, 15(3) pp. 3–26.
http://oro.open.ac.uk/34092
• 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

50. Social Network Analysis (SNAPP)
50Bakharia, A. and Dawson, S., SNAPP: a bird's-eye view of temporal participant interaction. In: Proceedings of the 1st
International Conference on Learning Analytics and Knowledge (Banff, Alberta, Canada, 2011). ACM. pp.168-173
What’s going on
in these discussion forums?

51. Social Network Analysis (SNAPP)
51
http://www.slideshare.net/aneeshabakharia/snapp-20minute-presentation

52. Social Network Analysis (SNAPP)
52
http://www.slideshare.net/aneeshabakharia/snapp-20minute-presentation
2 learners connect
otherwise separate
clusters
tutor only engaging
with active students,
ignoring disengaged
ones on the edge

53. Social Learning Analytics about to appear in
products…
53
http://www.desire2learn.com/products/analytics (this is from a beta demo)

54. Semantic Social Network Analytics
De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. Discourse-centric learning analytics. 1st
International Conference on Learning Analytics & Knowledge (Banff, 27 Mar-1 Apr, 2011) http://oro.open.ac.uk/25829

55. Visualizing and filtering social ties in
SocialLearn by topic and type
Schreurs B, Teplovs C, Ferguson R, De Laat M and Buckingham Shum S. (2013) Visualizing Social Learning Ties by Type and Topic:
Rationale and Concept Demonstrator. Proc. 3rd International Conference on Learning Analytics & Knowledge. Leuven, BE: ACM, 33-37.
Open Access Eprint: http://oro.open.ac.uk/36891

56. Visualizing and filtering social ties in
SocialLearn by topic and type

57. Visualizing and filtering social ties in
SocialLearn by topic and type

58. Visualizing and filtering social ties in
SocialLearn by topic and type

59. Visualizing and filtering social ties in
SocialLearn by topic and type

60. discourse analytics
are students using
language as a
knowledge-building
tool?
60

61. The promise of language technologies
Beyond number / size / frequency
of posts or ‘trending topic’
?
http://www.glennsasscer.com/wordpress/wp-content/uploads/2011/10/iceberg.jpg

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
-50
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
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. 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

67. “Rhetorical parsing” to identify constructions
signifying scholarly writing
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

68. 68
Xerox Incremental Parser (XIP)
Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search.
Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.

69. 69
Xerox Incremental Parser (XIP)
Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search.
Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.

70. Initial evaluation of XIP is promising,
but methodologically complex
Human analyst XIP
A striking example – but not all were like this (De Liddo et al, 2012)
19 sentences annotated 22 sentences annotated
11 sentences same as human annotation
71 sentences annotated 59 sentences annotated
42 sentences same as human annotation
Document 1
Document 2
Extract from annotation comparison:

71. Xerox Incremental Parser (XIP)
XIP’s raw output is fine for NLP
machines/researchers, but
not learner/educator
friendly

72. Xerox Incremental Parser (XIP)
5000 (or even 30) plain text files…
we need overviews
of XIP analyses from
a corpus

73. Making XIP analytics visible:
Annotations on the full text using the OU’s Cohere
social sensemaking app (Firefox add-on)

74. XIP Dashboard
All papers by year and concept, with
colour = concept density (v2 mockup)
74
Simsek D, Buckingham Shum S, Sándor Á, De Liddo A and Ferguson R. (2013) XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of
Scientific Metadiscourse. 1st International Workshop on Discourse-Centric Learning Analytics, at 3rd International Conference on Learning Analytics &
Knowledge. Leuven, BE (Apr. 8-12, 2013). Open Access Eprint: http://oro.open.ac.uk/37391

75. intrinsic motivation
self-regulation
resilience
75

76. Why do dispositions matter?
76
“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

77. “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
77
Interview with Carol Dweck:
http://interviewscoertvisser.blogspot.co.uk/2007/11/interview-with-carol-dweck_4897.html
Why do dispositions matter?

78. “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
78
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
Why do dispositions matter?

79. How can we model and
quantify learning
dispositions in order
to develop analytics?
79

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

81. 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)

82. Learning to Learn: 7 Dimensions of Learning Power
82

83. platforms for
Dispositional Learning
Analytics
83DLA Workshop, Stanford (July 2013) http://learningemergence.net/events/lasi-dla-wkshp

84. 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

85. ELLI generates cohort data for each
dimension
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

86. 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

87. 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

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

89. Could a platform generate an
ELLI profile from user traces?
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
Questioning and
challenging may
load onto Critical
Curiosity
Sharing relevant
resources from
other contexts
may load onto
Meaning Making
Repeated
attempts to pass
an online test
may load onto
Resilience

90. 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

91. thorny issues
91

92. 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

93. cf. Bowker and Starr’s “Sorting Things Out”
on classification schemes
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
“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.”

94. The Wal-Martification of education?
94http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college
http://lak12.wikispaces.com/Recordings
“The basic question is not
what can we measure?
The basic question is
what does a good
education look like?
Big questions.
“data narrowness”
“instrumental learning”
“students with no curiosity”

95. 95
“Our analytics are our
pedagogy”
(and epistemology)
They promote assessment regimes
— which drive (and strangle)
educational innovation
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

96. learning analytics are
not neutral
data does not “speak for itself”
96

97. Analytics cycle (Doug Clow)
h.p://www.slideshare.net/dougclow/the-­‐learning-­‐analy)cs-­‐cycle-­‐closing-­‐the-­‐loop-­‐effec)vely
(slide
5)
97

98. Analytics cycle (George Siemens)
h.p://www.slideshare.net/gsiemens/eli-­‐2012-­‐sensemaking-­‐analy)cs
(slide
7)
98

99. All analytics are infused with human values
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)
99
What kinds of learners?
What kinds of learning?
What data could be
generated digitally
from the use context?
(you can invent future
technologies if need)
Does your theory
predict patterns
signifying learning?
What human +/or
software
interventions /
recommendations?
How to render the analytics,
for whom, and will they
understand them?
What analytical tools
could be used to find
such patterns?
ethics

100. 100
http://www.flickr.com/photos/somegeekintn/3709203268/
Who gets to define,
and hold, the
magnifying glass?

101. 101
http://www.flickr.com/photos/centralasian/6396004353/sizes/m/in/photostream/
Learning Analytics should
provide mirrors for
learners to become more
reflective, and less
dependent

102. to go deeper…
102

103. Join the community…
103
http://SoLAResearch.org
http://LAKconference.org
replays of all
previous
conference
presentations

104. Join the community…
104
http://www.solaresearch.org/events/lasi
replays of all
sessions

105. Open course on systemic
deployment of analytics
105https://www.canvas.net/courses/policy-and-strategy-for-systemic-deployment-of-learning-analytics
Universities and
companies exploring
institutional strategy,
policy and
infrastructure

106. JISC Briefings on Learning Analytics
106http://publications.cetis.ac.uk/c/analytics

107. EDUCAUSE Briefings on Learning Analytics
107
http://www.educause.edu/library/learning-analytics

108. Learning Analytics Policy Brief
(UNESCO • IITE)
108http://bit.ly/LearningAnalytics

109. http://LearningEmergence.net

110. We all love a good big brother (don’t we?)
“A responsible school/university
in 2016 will use every form of
data shared by students in order
to maximise their success.”
Discuss
Thank you!