Slides for a talk at Bett 2015, London, on Friday 23 January at Excel. Learning analytics has great potential. By using data more effectively, we can understand and improve learning and the learning environment. Trail-blazing projects, exciting demonstrations and earnest strategy papers set out a compelling vision for data in HE. That vision can sometimes seem far from institutional reality. How can we get some of those benefits for our learners? This interactive workshop will help participants assess their institution’s current capability for making use of learning analytics, and help them plan for action. The facilitators will draw on a wide range of practical experience, including from the pan-European Learning Analytics Community Exchange project.

1. Creating an action plan
for learning analytics
Dr Doug Clow
Institute of Educational Technology, The Open University, UK
@dougclow
dougclow.org
doug.clow@open.ac.uk

2. 2
2
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3. Where are you starting from? (1)
a) Manager
b) Academic / lecturer / teacher
c) L&T / acad developer / ed tech / IT
d) Vendor / consultant
e) Something else
cc licensed ( BY ) flickr photo by Swaminathan: http://flickr.com/photos/araswami/2168316216/

4. Photo (CC)-BY Robert Couse-Baker https://www.flickr.com/photos/29233640@N07/15551695380
Where are you starting from? (2)
a) I have no idea about learning analytics
b) I’m dipping my toe in the water
c) I’m doing a few little things
d) I’m making significant efforts
e) I could be giving this talk

5. 1. Where do you want to get to?
2. Where are you now?
3. What are the next steps?

6. 1. Where do you want to get to?
2. Where are you now?
3. What are the next steps?

7. What is learning analytics?
• the measurement, collection, analysis and reporting of
data about learners and their contexts, for purposes of
understanding and optimising learning and the
environments in which it occurs
– First International Conference on Learning Analytics And Knowledge (LAK11), Banff, Alberta, Feb 27-
Mar 1, 2011
Photo (CC)-BY Cris: http://flickr.com/photos/chrismatos/6917786197/

8. Photo public domain: http://commons.wikimedia.org/wiki/File:DESYNebelkammer.jpg
- Erik Duval
http://erikduval.wordpress.com/2012/01/30/learning-
analytics-and-educational-data-mining/
“collecting traces
that learners leave
behind and using
those traces to
improve learning”

9. Clow, LAK12, 2012

10. examples

11. University Dashboard (Google Image search)
11

12. Predictive modelling
• Place students in one of three risk groups
=> traffic light
• Trigger for interventions
• Retention and grade improvements

13. Image (cc) Darwin Bell http://www.flickr.com/photos/darwinbell/296553221/
“The predictive model
was used as a trigger for
intervention emails to the
student.”
13

14. Image (cc) Darwin Bell http://www.flickr.com/photos/darwinbell/296553221/
From:
DONOTREPLY@mail.example.com
You are in trouble. The
computer predictive model
gives you a 87.4322% chance
of failing this course. You
must see a teacher
immediately.
14

15. Image (cc) Darwin Bell http://www.flickr.com/photos/darwinbell/296553221/
From:
DONOTREPLY@mail.example.com
You are in trouble. The
computer predictive model
gives you a 87.4322% chance
of failing this course. You
must see a teacher
immediately.
15
Hi Alex
Are you Ok? I noticed you
haven’t logged on this week, and I
know you struggled with the last
assessment. We can work through
this together - let’s have a chat as
soon as possible.
Pat.

16. Course choice
• Degree requirements
• Your record to date
• Previous student success
16
Personalised study choice recommendations
Photo (CC)-BY-SA Lauren Macdonald https://www.flickr.com/photos/42386632@N00/8528725328

17. Social Network Analysis
• Social Networks Adapting Pedagogic Practice
• Network visualisations of forum activity data from VLE
• See patterns
• Spot central and
disconnected
• Identify at-risk
• Improve teaching

19. what else could you do?

20. What data do we have about learners?
• Demographics
• Previous educational experience
• Grades, scores, achievements, struggles
• Attendance, location, gaze
• Software logs
• Online tracking
• Other online activity (tracking)
• … more every week.
20
Photo (CC)-BY-SA AJ Cann https://www.flickr.com/photos/ajc1/15574010080/

21. What can we do with that data?
• Identify learners who need help
– Simple or predictive
• Trigger interventions
– Via teacher, or direct
• Learn which interventions work
• Build a complete cognitive learning system
• Suggest resources or source of help
– Learners like you found this helpful
– This person might be able to help you
21
Photo (CC)-BY-NC Pulpolux https://www.flickr.com/photos/pulpolux/8735428280

22. 1. Where do you want to get to?
What’s the one thing you most
want to do at your institution?

23. 1. Where do you want to get to?
2. Where are you now?
3. What are the next steps?

24. 24
Greller & Drachsler (ET&S, 2012)

25. Stakeholders
• Institution
– Senior management
– Deans, Heads of Department
• Teachers
• Learners
• Others
– IT, Learning & Teaching Centre,
Registry, Library, Estates
25
Photo (CC)-BY-NC-SA David Kracht https://www.flickr.com/photos/dave_kr8/15158177186

26. Internal limitations
• Competences
– Specification
– Deployment
– Maintenance
– Data interpretation
– Action
• Acceptance
• Resources
26
Photo (CC)-BY-NC UK Ministry of Defence https://www.flickr.com/photos/defenceimages/11052581603

27. External constraints
• Conventions (ethics)
– privacy, accessibility, equality and diversity,
transparency, accountability
• Norms (legislation, policy)
– Data Protection, Freedom of Information,
Equality and Diversity
27
Photo (CC)-BY-NC-ND Massmo Relsig https://www.flickr.com/photos/99574551@N04/9622288599

28. 28
Does your university learn about its students?
Data stored
Changed
capacity to act
Photo (CC)-BY-NC Steve Evans https://www.flickr.com/photos/babasteve/15531002668
Understanding

29. 29
Greller & Drachsler (ET&S, 2012)

30. 2. Where are you now?
What’s the biggest challenge at
your institution?

31. OU Analytics Project
Intervention and Evaluation
Data Visualisations
Ethics Framework
Predictive Modelling
Learning Experience Data
Professional Development
Small Data Student Tools

32. Enrolment Attendance Submission MarksUsage
Programme
Courses & Units
Mandatory
Sessions Resources Assignments
Leader Tutor Marker
Board
Outcome
Department
HeadDean
Faculty
Left, Failed, Withdrew
Resit Work
Timetable
Curriculum
Hierarchy
Learning
Resources
Provider
Type
Assessment
Staff
Admission
Entry
Targets
Entry
Quals.
Application
Enquiry
Bio-
demographics
Student
R&A
Course Entry
Employment or
Study (elsewhere)
Satisfaction
Questions
Surveys
Type
AccommodationProgression
Level &
Subject Grad
Data Warehouse Lines of Enquiry
Student
R&A Course Entry
Engagement
Curriculum Hierarchy
Staff
Partial coverage
Opening 2015
Opening 2015
Terminating at Course
Delays expected
Timetable
Surveys
Learning Resources
Assessment
Opening 2015
Partial coverage
Opening 2015
Good coverage
Further Study
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International licence | Mark Stubbs (@thestubbs)
University
Laying foundations for Learning Analytics at MMU

33. 1. Where do you want to get to?
2. Where are you now?
3. What are the next steps?

34. www.laceproject.eu
Learning Analytics Community Exchange (FP7)
• Coordination and Support
• Evidence Hub
• Events
• Publications, briefings, webinars

35. www.laceproject.eu
• Blog bit.ly/lace-blog
• Newsletter bit.ly/lace-newsletter
• FAQs bit.ly/lace-faqs
• Learning Analytics Review
bit.ly/lace-review-papers
• Become an Associate Partner!

36. 36
• Current state of play in UK HE & FE
• Code of practice for learning analytics
• Network meeting, 20 Feb 2014, UEL
http://analytics.jiscinvolve.org/
Effective Learning Analytics

37. • LAK conferences
• LASI workshops
• Flare local meetings
• Storm PhD training
• Journal of LA
• … and more!
www.solaresearch.org

38. Photo (CC)-BY J. Aaron Farr on Flickr https://www.flickr.com/photos/jaaronfarr/2372892211
Learning Analytics Masters
Program (LAMP)
Open Learning Analytics (OLA)

39. 3. What are the next steps?
What one specific, concrete
thing will you do next?

40. Organising
academics is
like herding
cats …
but they will
come if you
leave a saucer
of cream.
- Lewis Elton
Photo (CC)-BY-ND Brian Leon https://www.flickr.com/photos/ncbrian/1459269613

41. Photo (CC)-BY Steve Dunleavy: http://flickr.com/photos/stevedunleavy/5142841381/
The journey of a thousand miles
begins with a single step.
– Laozi , Tao Te Ching

42. Test what you do
• Does it work?
• You’ll have data!
Photo (CC)-BY Kevin Dooley https://www.flickr.com/photos/pagedooley/6613526021/
Towards Evidence-
Based Practice

43. www.laceproject.eu
@laceproject
43
LACE Annual Meeting: Lunch!
1.30pm, Fox Bar, just outside the building

44. Thanks to:
People:
• LACE at the OU: Rebecca Ferguson, Bart Rientes,
Simon Cross, Linda Norwood Michelle Bailey,
Rebecca Wilson, Evaghn De Souza, Natalie
Eggleston, Oliver Millard, Gary Elliot-Citigottis.
• LACE project partners: CETIS (Bolton), OUNL,
Skolverket, HIOA, Kennisnet, ITS, ATiT.
• The learning analytics community, including SoLAR,
IEDMS, those I’ve met at LAK and LASI
• Bett and venue staff
Funders:
• LACE: European Commission 619424-FP7-ICT-2013-11

45. “Learning Analytics: Making Learning Better?” by Doug Clow,
Institute of Educational Technology, The Open University,
was presented at Bett, London, on 23 January 2015.
@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.
www.laceproject.eu
@laceproject
45

46. cc licensed ( BY ) flickr photo by David Goehring: http://flickr.com/photos/carbonnyc/33413040/