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Evidence Hub 
activity 
LACE SoLAR Flare 
24 October 2014 
The Open University, Milton Keynes, UK 
Co-Chairs: 
Doug Clow, Rebecca Ferguson, Simon Cross
“Big data is like teenage sex: everyone 
talks about it, nobody really knows how 
to do it, everyone thinks everyone else 
is doing it, so everyone claims they are 
doing it…” 
– Dan Ariely, Facebook, 6 Jan 2013
“Big data is like teenage sex: everyone 
talks about it, nobody really knows how 
to do it, everyone thinks everyone else 
is doing it, so everyone claims they are 
doing it…” 
– Dan Ariely, Facebook, 6 Jan 2013 
… and the world of education seems 
obsessed about it, but the little that 
does go on is often done badly, and 
leaves people disillusioned.
4 
Nobody seems to 
know what they’re 
talking about … 
Let’s change that! 
Juvenile boat-tailed grackles, Quiscalus major 
Photo (CC)-BY-SA Andrea Westmoreland https://www.flickr.com/photos/andrea_pauline/4768171665
5 
What do we know, 
collectively? 
What do we not know? 
[T]here are known knowns; there are things we 
know we know. We also know there are known 
unknowns; that is to say we know there are some 
things we do not know. But there are also unknown 
unknowns - the ones we don't know we don't know. 
- Donald Rumsfeld 
Photo public domain http://en.wikipedia.org/wiki/File:Donald_Rumsfeld_Tommy_Franks.jpg
6 
hypotheses 
• claims 
• research questions 
• propositions 
Evidence is for or against … 
Photo (CC)-BY Brian Hillegas https://www.flickr.com/photos/seatbelt67/502255276
Hypothesis A: Learning 
Learning is at the heart of learning analytics. Do we see real improvements in learning 
outcomes for learners? We might be able to see patterns in learners’ data, but can we take 
action based on those patterns that improves their learning? We might be able to 
personalise learning based on learners’ data, but does that make any difference to how 
much they learn? 
This hypothesis is about improved learning outcomes: e.g. cognitive gains, improved 
assessment marks, better scores on tests, attainment results. 
Example positive evidence 
A study showing measurable improvements in scores on a test among learners who 
received study prompts from a learning analytics system compared to the usual teaching 
approach. 
Example negative evidence 
A study showing no significant difference in assessment results before and after the 
introduction of a learning analytics dashboard. 
7 
Learning analytics improves learning outcomes.
Hypothesis B: Teaching 
Does learning analytics optimise the learning process? We would expect that to lead to 
more efficient processes, allow resources to be better targeted, and save money and time. 
We’d also expect performance metrics (other than attainment) to improve. Does learning 
analytics lead to improvements in retention, completion and progression? 
This hypothesis is about improvements to teaching and learning that are not direct 
learning gains by the learner. 
Example positive evidence 
A study showing improved retention among student cohorts whose tutors were prompted 
to contact at-risk learners identified by a predictive model compared to other cohorts. 
Example negative evidence 
A learning analytics project that increased costs but resulted in no improvements in 
efficiency or performance. 
8 
Learning analytics improves learning support and teaching, 
including retention, completion and progression.
Hypothesis C: Uptake 
Is learning analytics a fad that will never really get off the ground at real scale? Will it ever 
move beyond pilot projects and demos? If a system is deployed across a whole 
organisation, do the teachers and learners actually use it? 
This hypothesis looks at the level of usage of learning analytics, and is concerned with 
institutional and policy perspectives. 
Example positive evidence 
A survey of usage of learning analytics dashboards across the university sector in one 
country finds them in use in more than half of organisations. 
Example negative evidence 
A predictive modelling project is rolled out across a group of schools, but usage is low and 
the project is discontinued. 
9 
Learning analytics are taken up and used widely, 
including deployment at scale.
Hypothesis D: Ethics 
Learning analytics raises many ethical issues, around privacy, transparency, surveillance, 
data ownership and control, and data protection. Can these real concerns be addressed 
effectively, or will they prove to be barriers? 
This hypothesis is about the ‘should we’ questions, rather than the ‘can we’ ones 
addressed by the other hypotheses. 
Example positive evidence 
An organisation develops an ethics policy for learning analytics that is warmly received by 
learners and other stakeholders. 
Example negative evidence 
A large-scale project to gather analytics data across multiple schools is shut down because 
of concerns about privacy. 
10 
Learning analytics is used in an ethical way.
LIVE DEMO
Your mission 
• Engage with these questions 
• Pool our expertise 
• Inform, discuss, debate 
(CC) Mike Licht on Flickr http://www.flickr.com/photos/notionscapital/4436135087/
Your task: build a SoLAR system 
• You each have two hypotheses 
• What evidence do you know in favour? Against? 
– Wide definition – “this systematic review of RCTs in Science proved 
it” to”I’ve heard of someone who is doing this and it seems to be Ok” 
• Write on Post-Its: 
– Positive = yellow, Negative = green 
Photo (CC)-BY Image Editor https://www.flickr.com/photos/11304375@N07/2818891443
Go to your room 
• Lists on the board outside 
• Finish at 3.30 and come back here. 
• I’ll circulate, take photos, and summarise.
www.laceproject.eu 
@laceproject 
“Evidence Hub Activity” by Doug Clow, Institute of Educational 
Technology, The Open University, was presented at the LACE 
SoLAR Flare, The Open University, Milton Keynes, UK, on 24 
October 2014. 
@dougclow 
dougclow.org 
doug.clow@open.ac.uk 
This work was undertaken as part of the LACE Project, supported by the European Commission Seventh 
Framework Programme, grant 619424. 
These slides are provided under the Creative Commons Attribution Licence: 
http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms. 
15

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Evidence Hub Activity

  • 1. Evidence Hub activity LACE SoLAR Flare 24 October 2014 The Open University, Milton Keynes, UK Co-Chairs: Doug Clow, Rebecca Ferguson, Simon Cross
  • 2. “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…” – Dan Ariely, Facebook, 6 Jan 2013
  • 3. “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…” – Dan Ariely, Facebook, 6 Jan 2013 … and the world of education seems obsessed about it, but the little that does go on is often done badly, and leaves people disillusioned.
  • 4. 4 Nobody seems to know what they’re talking about … Let’s change that! Juvenile boat-tailed grackles, Quiscalus major Photo (CC)-BY-SA Andrea Westmoreland https://www.flickr.com/photos/andrea_pauline/4768171665
  • 5. 5 What do we know, collectively? What do we not know? [T]here are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns - the ones we don't know we don't know. - Donald Rumsfeld Photo public domain http://en.wikipedia.org/wiki/File:Donald_Rumsfeld_Tommy_Franks.jpg
  • 6. 6 hypotheses • claims • research questions • propositions Evidence is for or against … Photo (CC)-BY Brian Hillegas https://www.flickr.com/photos/seatbelt67/502255276
  • 7. Hypothesis A: Learning Learning is at the heart of learning analytics. Do we see real improvements in learning outcomes for learners? We might be able to see patterns in learners’ data, but can we take action based on those patterns that improves their learning? We might be able to personalise learning based on learners’ data, but does that make any difference to how much they learn? This hypothesis is about improved learning outcomes: e.g. cognitive gains, improved assessment marks, better scores on tests, attainment results. Example positive evidence A study showing measurable improvements in scores on a test among learners who received study prompts from a learning analytics system compared to the usual teaching approach. Example negative evidence A study showing no significant difference in assessment results before and after the introduction of a learning analytics dashboard. 7 Learning analytics improves learning outcomes.
  • 8. Hypothesis B: Teaching Does learning analytics optimise the learning process? We would expect that to lead to more efficient processes, allow resources to be better targeted, and save money and time. We’d also expect performance metrics (other than attainment) to improve. Does learning analytics lead to improvements in retention, completion and progression? This hypothesis is about improvements to teaching and learning that are not direct learning gains by the learner. Example positive evidence A study showing improved retention among student cohorts whose tutors were prompted to contact at-risk learners identified by a predictive model compared to other cohorts. Example negative evidence A learning analytics project that increased costs but resulted in no improvements in efficiency or performance. 8 Learning analytics improves learning support and teaching, including retention, completion and progression.
  • 9. Hypothesis C: Uptake Is learning analytics a fad that will never really get off the ground at real scale? Will it ever move beyond pilot projects and demos? If a system is deployed across a whole organisation, do the teachers and learners actually use it? This hypothesis looks at the level of usage of learning analytics, and is concerned with institutional and policy perspectives. Example positive evidence A survey of usage of learning analytics dashboards across the university sector in one country finds them in use in more than half of organisations. Example negative evidence A predictive modelling project is rolled out across a group of schools, but usage is low and the project is discontinued. 9 Learning analytics are taken up and used widely, including deployment at scale.
  • 10. Hypothesis D: Ethics Learning analytics raises many ethical issues, around privacy, transparency, surveillance, data ownership and control, and data protection. Can these real concerns be addressed effectively, or will they prove to be barriers? This hypothesis is about the ‘should we’ questions, rather than the ‘can we’ ones addressed by the other hypotheses. Example positive evidence An organisation develops an ethics policy for learning analytics that is warmly received by learners and other stakeholders. Example negative evidence A large-scale project to gather analytics data across multiple schools is shut down because of concerns about privacy. 10 Learning analytics is used in an ethical way.
  • 12. Your mission • Engage with these questions • Pool our expertise • Inform, discuss, debate (CC) Mike Licht on Flickr http://www.flickr.com/photos/notionscapital/4436135087/
  • 13. Your task: build a SoLAR system • You each have two hypotheses • What evidence do you know in favour? Against? – Wide definition – “this systematic review of RCTs in Science proved it” to”I’ve heard of someone who is doing this and it seems to be Ok” • Write on Post-Its: – Positive = yellow, Negative = green Photo (CC)-BY Image Editor https://www.flickr.com/photos/11304375@N07/2818891443
  • 14. Go to your room • Lists on the board outside • Finish at 3.30 and come back here. • I’ll circulate, take photos, and summarise.
  • 15. www.laceproject.eu @laceproject “Evidence Hub Activity” by Doug Clow, Institute of Educational Technology, The Open University, was presented at the LACE SoLAR Flare, The Open University, Milton Keynes, UK, on 24 October 2014. @dougclow dougclow.org doug.clow@open.ac.uk This work was undertaken as part of the LACE Project, supported by the European Commission Seventh Framework Programme, grant 619424. These slides are provided under the Creative Commons Attribution Licence: http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms. 15

Editor's Notes

  1. It is my very huge pleasure to welcome you all. Here and online.
  2. And this is a terrible shame because with the right techniques and the right context, where there’s trust and enthusiastic consent, it can be pretty good.
  3. Avoid Unknown knowns – things we know but don’t realise we do, like whether there are WMD in Iraq.
  4. )