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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
mission
walk out with
better questions
than you can ask right now about analytics
new tech and collaboration opportunities
to advance
education 2
why are we seeing this?...
3
Why are we seeing this?...
4
VLEs + Analytics Publishers + Analytics
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?...
6https://www.edx.org/about
“this is big data, giving
us the chance to ask big
questions about
learning”
Why are we seeing this?...
7http://careers.stackoverflow.com/jobs/35348/software-engineer-analytics-coursera
Why are we seeing this?...
8
Why are we seeing this?...
http://www.independent.ie/lifestyle/education/trinity-joins-elite-colleges-to-offer-free-online-courses-29355438.html
the data/analytics
tsunami is about to hit
the education sector
9
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…
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
Continuous coverage…
http://www.online-educa.com/OEB_Newsportal/whats-so-big-about-big-data
13
…and debated…
http://www.online-educa.com/OEB_Newsportal/we-urgently-need-to-safeguard-free-will-in-the-age-of-big-data
14
15
Tectonic forces are reshaping the
learning landscape…
16
the opportunity for
learning design
learning sciences
17
From an analytics product review…
18
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
Aquarium Analytics!
20
21
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
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
Back to Aquarium Analytics…
25
fish
aquarium science
learners?
learning science
instructional design
Back to Aquarium Analytics…
Purdue University Signals: real time traffic-
lights for students based on predictive model
26
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
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
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	
  
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
the opportunity for the
learning sciences
to combine with your university’s
collective
intelligence
31
macro
meso
micro
analytics
32
Macro/Meso/Micro Learning Analytics
Macro:
region/state/national/international
League Tables
Data
Interoperability
Initiatives
Macro/Meso/Micro Learning Analytics
Meso:
institution-wide
Macro:
region/state/national/international
Business
Intelligence
Products
Business Intelligence
≠
Learning Analytics
Micro:
individual user actions
(and hence cohort)
Macro/Meso/Micro Learning Analytics
Meso:
institution-wide
Macro:
region/state/national/international
Learning
Analytics
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
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
Insight Centre
intersection with
learning analytics?
39
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
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/
predictive models
are exciting
but there are many other
kinds of analytics
42
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
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
Khan Academy
http://www.youtube.com/watch?v=DLt6mMQH1OY
Khan Academy has extended great instructional
movies with a tutoring platform with detailed analytics
46
https://grockit.com/research
Adaptive platforms generate fine-grained
analytics on curriculum mastery
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
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
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
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?
Social Network Analysis (SNAPP)
51
http://www.slideshare.net/aneeshabakharia/snapp-20minute-presentation
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
Social Learning Analytics about to appear in
products…
53
http://www.desire2learn.com/products/analytics (this is from a beta demo)
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
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
Visualizing and filtering social ties in
SocialLearn by topic and type
Visualizing and filtering social ties in
SocialLearn by topic and type
Visualizing and filtering social ties in
SocialLearn by topic and type
Visualizing and filtering social ties in
SocialLearn by topic and type
discourse analytics
are students using
language as a
knowledge-building
tool?
60
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
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?
-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
-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
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
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
“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
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
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.
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:
Xerox Incremental Parser (XIP)
XIP’s raw output is fine for NLP
machines/researchers, but
not learner/educator
friendly
Xerox Incremental Parser (XIP)
5000 (or even 30) plain text files…
we need overviews
of XIP analyses from
a corpus
Making XIP analytics visible:
Annotations on the full text using the OU’s Cohere
social sensemaking app (Firefox add-on)
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
intrinsic motivation
self-regulation
resilience
75
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
“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?
“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?
How can we model and
quantify learning
dispositions in order
to develop analytics?
79
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
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)
Learning to Learn: 7 Dimensions of Learning Power
82
platforms for
Dispositional Learning
Analytics
83DLA Workshop, Stanford (July 2013) http://learningemergence.net/events/lasi-dla-wkshp
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
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
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
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
EnquiryBlogger
dashboard – direct
navigation to learner’s
blogs from the visual
analytic
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
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
thorny issues
91
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
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.”
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
“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
learning analytics are
not neutral
data does not “speak for itself”
96
Analytics cycle (Doug Clow)
h.p://www.slideshare.net/dougclow/the-­‐learning-­‐analy)cs-­‐cycle-­‐closing-­‐the-­‐loop-­‐effec)vely	
  (slide	
  5)	
  
97
Analytics cycle (George Siemens)
h.p://www.slideshare.net/gsiemens/eli-­‐2012-­‐sensemaking-­‐analy)cs	
  (slide	
  7)	
  
98
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
http://www.flickr.com/photos/somegeekintn/3709203268/
Who gets to define,
and hold, the
magnifying glass?
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
to go deeper…
102
Join the community…
103
http://SoLAResearch.org
http://LAKconference.org
replays of all
previous
conference
presentations
Join the community…
104
http://www.solaresearch.org/events/lasi
replays of all
sessions
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
JISC Briefings on Learning Analytics
106http://publications.cetis.ac.uk/c/analytics
EDUCAUSE Briefings on Learning Analytics
107
http://www.educause.edu/library/learning-analytics
Learning Analytics Policy Brief
(UNESCO • IITE)
108http://bit.ly/LearningAnalytics
http://LearningEmergence.net
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!

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insight-centre-galway-learning-analytics

  • 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
  • 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?...
  • 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)
  • 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
  • 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…
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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!