Confronting Reality with Big Data & Learning Analytics We are experiencing an explosion in the quantity of data available online from archives and live streams. Learning Analytics is concerned with how educational research, and learning platform design, can make more effective use of such data (Long & Siemens, 2011). Improving outcomes through the analysis of data is of interest to researchers, administrators, systems architects, social media developers, educators and learners. Analytics are being held up by some as a way to confront, and tackle, the tough new realities of less money, less attention, and higher accountability for quality of learning. Researchers and vendors are building reporting capabilities into tools that provide unprecedented levels of data on learners. This symposium will show what is possible, and what's coming soon. What objections could possibly be raised to such progress? However, information infrastructure embodies and shapes worldviews: classification schemes are not only systematic ways to capture and preserve, but also to forget, by virtue of what remains invisible (Bowker & Star, 1999). Learning analytics and recommendation engines are designed with a particular conception of ‘success’, driving the patterns deemed to be evidence of progress, the interventions that are deemed appropriate, the data captured and the rules that fire in software. This symposium will air some of the critical arguments around the limits of decontextualised data and automated analytics, which often appear reductionist in nature, failing to illuminate higher order learning. There are complex ethical issues around data fusion, and it is not clear to what extent learners are empowered, in contrast to being merely the objects of tracking technology. Educators may also find themselves at the receiving end of a new battery of institutional ‘performance indicators’ that do not reflect what they consider to be authentic learning and teaching. This Symposium will provide the opportunity to hear a series of brief presentations introducing contrasting perspectives, before the debate is opened to all. Speakers from a cross-section of The Open University will describe how we are connecting datasets, analysing student data and prototyping next generation analytics. Complementing this, JISC will present a national capability perspective, with an update on the JISC CETIS ‘landscape analysis’ of the field, which will clarify potential benefits, issues to consider, and help institutions to assess their current capability and possible next steps. Participants will catch up with developments in this fast moving field, through exposure to the possibilities of analytics, as well as issues to be alert to.

1. ALT-C 2012, Manchester — “A Confrontation with Reality” @sbskmi
@R3beccaF
@kevinmayles
Symposium: Confronting Reality with… @sheilmcn
Big Data & @richardn2009
Learning Analytics
http://altc2012.alt.ac.uk/talks/28051
Simon Buckingham Shum, Naomi Jeffery,
Kevin Mayles, Richard Nurse & Rebecca Ferguson
The Open University (KMI, IET, LTS & Library)
Sheila MacNeill
JISC CETIS

2. Symposium: Confronting Reality with…
Big Data & Learning Analytics
intro
Simon Buckingham Shum
2

3. John Daniel
http://www.col.org/resources/speeches/2012presentations/Pages/2012-02-01.aspx
3

4. Possibly 90% of the digital data we have
today was generated in the last 2 years
Volume The sheer amount of data outstrips old infrastructure capacity
Variety Internet of things, e-business transactions, environmental sensors,
social media, audio, video, mobile…
Velocity The speed of data access and analysis is exploding
A quantitative shift of this scale is in fact a qualitative shift, requiring
new ways of thinking about societal phenomena
4

5. edX: “this is big data giving us the chance to
ask big questions about learning”
5

6. US states are getting the infrastructure in place to
share educational data across the silos
dataqualitycampaign.org
6

7. Analytics in your VLE:
Blackboard: feedback to students
http://www.blackboard.com/Platforms/Analytics/Overview.aspx
7

8. Analytics in your VLE:
Desire2Learn visual analytics & predictive models
http://www.desire2learn.com/products/analytics
Students
Online tools
8

9. Adaptive platforms (ITS comes of age) generate
fine-grained analytics
http://knewton.com

10. Adaptive platforms (ITS comes of age) generate
fine-grained analytics
http://oli.cmu.edu

11. Purdue University Signals
Real time traffic-lights for students
based on a predictive model
Premise: academic success is defined as a function of
aptitude (as measured by standardized test scores and
similar information) and effort (as measured by participation
within the CMS).
Using factor analysis and logistic regression, a model was
tested to predict student success based on:
• ACT or SAT score
• Overall grade-point average
Predicted 66%-80% • CMS usage composite
of struggling • CMS assessment composite
students who • CMS assignment composite
needed help • 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 11

12. The Wal-Martification of education?
“What counts as
data, how do you get
it, and what does it
actually mean?”
“The basic question is not
what can we measure?
The basic question is
“data narrowness” what does a good
“instrumental learning” education look like?
“students with no curiosity” Big questions.
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http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college/

13. It’s about insight and sensemaking, not data
Al Essa: Learning Analytics… less
data, more insight. Analytics primary
task is not to report the past, but to
help find the optimal path to a
desired future
George Siemens: Analytics doesn’t
end with the data dashboard – that’s
when it really starts – it’s all about
sensemaking

14. Symposium: Confronting Reality with…
Big Data & Learning Analytics
JISC
surveying the
UK landscape
Sheila McNeill
14

15. Confronting Big Data and
Learning Analytics
ALT-C 2012
Sheila MacNeill
Assistant Director

16. Big data and analytics in education
n Shift from data collecting to data connecting
n Develop data informed mind–sets
n Integration of multiple (structured and unstructured) data
sources
n Management and use of real-time data
n ((http://blogs.cetis.ac.uk/cetisli/2011/12/14/big-data-and-
analytics-in-education-and-learning/)

17. JISC Cetis view of the landscape
Business
Intelligence
Learning
CRM
Analytics

18. practice
research
management
data

19. Analytics Reconnoitre
n Practical guidance of/for uses of analytics
n Who, why, what, where, when and how
n Audience: first movers and early adopters
n Topics: whole institutional issues, research, teaching and
learning, legal and ethical issues, skills & literacies, professional
development, technology and infrastructure
n http://jisc.cetis.ac.uk/topic/analytics

20. Symposium: Confronting Reality with…
Big Data & Learning Analytics
open.edu
VLE
perspective
Kevin Mayles
20

21. VLE
Analy*cs
@
the
OU
Virtual
Learning
Environment
Data
Warehouse
Usage
sta*s*cs
at
system,
faculty
and
‘Par*cipa*on
Tracking’
func*on
to
track
module
level
–
general
paAerns
individual
students’
interac*on
with
specific
online
learning
ac*vi*es
In
pilot
2012/13

22. Symposium: Confronting Reality with…
Big Data & Learning Analytics
open.edu
Library
perspective
Richard Nurse
22

23. Learning Analytics – the Library dimension
Student achievement
Recommender services
Library use
‘Students who looked at this article also
looked at this article’
‘Students on your course are looking at
these articles’
Library Impact Data Project
– Huddersfield University
http://www.flickr.com/photos/davepattern/6928727645/sizes/o/in/photostream/

24. Symposium: Confronting Reality with…
Big Data & Learning Analytics
open.edu
social learning
analytics
Rebecca Ferguson
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25. Social learning analytics
focus on how learners build knowledge
together in their cultural and social settings
Network analytics help me identify
• People with relevant interests
• People who support my learning
Discourse analytics help me locate
• Challenges and Extensions
• Evaluation and Reasoning

26. Symposium: Confronting Reality with…
Big Data & Learning Analytics
open.edu
bringing it all
together
Naomi Jeffrey
26

29. Symposium: Confronting Reality with…
Big Data & Learning Analytics
the floor is yours…
Does this excite or disturb you?
Who doesn’t want education to be evidence-based and high impact?
Who gets to see – and define – analytics?
What does ‘good’ learning look like in an analytics dashboard?
How might analytics be misinterpreted?
What ethical issues arise?
Your point or question…
29

30. Symposium: Confronting Reality with…
Big Data & Learning Analytics
join the
community…
30

31. SoLAResearch.org
UK SoLAR “Flare” 3rd Int. Conf. Learning
(national meetup) Analytics & Knowledge
Mon 19 Nov LAK13, Leuven
Open University 8-12 April 2013
Co-sponsored by OU & JISC
@SoLAResearch lakconference.org
#LearningAnalytics @LAKconf
http://jisc.cetis.ac.uk/topic/analytics
www.educause.edu/library/analytics
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