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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.
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Simon Buckingham Shum, Naomi Jeffery, Kevin Mayles, Richard Nurse & Rebecca Ferguson The Open University (KMI, IET, LTS & Library)
Sheila MacNeill JISC CETIS
Symposium: Confronting Reality with…
Big Data & Learning Analytics http://altc2012.alt.ac.uk/talks/28051
ALT-C 2012, Manchester — “A Confrontation with Reality” @sbskmi @R3beccaF @kevinmayles @sheilmcn @richardn2009
intro
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Symposium: Confronting Reality with…
Big Data & Learning Analytics
Simon Buckingham Shum
John Daniel
3 http://www.col.org/resources/speeches/2012presentations/Pages/2012-02-01.aspx
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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
edX: “this is big data giving us the chance to ask big questions about learning”
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US states are getting the infrastructure in place to share educational data across the silos
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dataqualitycampaign.org
Analytics in your VLE: Blackboard: feedback to students
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http://www.blackboard.com/Platforms/Analytics/Overview.aspx
Analytics in your VLE: Desire2Learn visual analytics & predictive models
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http://www.desire2learn.com/products/analytics
Students
Online tools
Adaptive platforms (ITS comes of age) generate fine-grained analytics
http://knewton.com
Adaptive platforms (ITS comes of age) generate fine-grained analytics
http://oli.cmu.edu
Purdue University Signals Real time traffic-lights for students based on a predictive model
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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 • 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
Predicted 66%-80% of struggling students who needed help
The Wal-Martification of education?
12 http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college/
“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
what does a good education look like?
Big questions.
“data narrowness” “instrumental learning”
“students with no curiosity”
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
JISC surveying the UK landscape
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Symposium: Confronting Reality with…
Big Data & Learning Analytics
Sheila McNeill
Confronting Big Data and Learning Analytics
ALT-C 2012
Sheila MacNeill Assistant Director
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/)
JISC Cetis view of the landscape
Business Intelligence
Learning Analytics CRM
practice
research
management
data
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
open.edu VLE
perspective 20
Symposium: Confronting Reality with…
Big Data & Learning Analytics
Kevin Mayles
VLE Analy*cs @ the OU
Virtual Learning
Environment Data Warehouse
Usage sta*s*cs at system, faculty and module level – general paAerns
‘Par*cipa*on Tracking’ func*on to track individual students’ interac*on with specific
online learning ac*vi*es In pilot 2012/13
open.edu Library
perspective 22
Symposium: Confronting Reality with…
Big Data & Learning Analytics
Richard Nurse
Learning Analytics – the Library dimension
http://www.flickr.com/photos/davepattern/6928727645/sizes/o/in/photostream/
Library Impact Data Project – Huddersfield University
‘Students who looked at this article also looked at this article’
‘Students on your course are looking at these articles’
Student achievement
Library use
Recommender services
open.edu social learning
analytics 24
Symposium: Confronting Reality with…
Big Data & Learning Analytics
Rebecca Ferguson
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
focus on how learners build knowledge together in their cultural and social settings
Social learning analytics
open.edu bringing it all
together 26
Symposium: Confronting Reality with…
Big Data & Learning Analytics
Naomi Jeffrey
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…
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Symposium: Confronting Reality with…
Big Data & Learning Analytics
join the community…
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Symposium: Confronting Reality with…
Big Data & Learning Analytics
UK SoLAR “Flare” (national meetup) Mon 19 Nov
Open University Co-sponsored by OU & JISC
@SoLAResearch #LearningAnalytics
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SoLAResearch.org
3rd Int. Conf. Learning Analytics & Knowledge LAK13, Leuven
8-12 April 2013
lakconference.org @LAKconf
www.educause.edu/library/analytics
http://jisc.cetis.ac.uk/topic/analytics
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