The document provides an overview of learning analytics work in Europe from several countries including Estonia, the Netherlands, and France. In Estonia, key initiatives discussed include the educational cloud and eDidaktikum teacher learning environment which incorporate learning analytics dashboards. In the Netherlands, the focus is on collaboration between universities, Apereo, and SURF. In France, recent and current national projects exploring learning analytics are highlighted such as Péricles and Hubble. The document concludes with an overview of the goals of the LACE project which aims to build bridges between learning analytics research, policy, and practice across Europe.

1. Learning Analytics:
European Perspectives
Rebecca Ferguson, The Open University, UK
Learning Analytics Community Exchange (LACE)
#laceproject

2. Gathering evidence
http://evidence.laceproject.eu/

3. Propositions
http://evidence.laceproject.eu/

4. Exploring the evidence
http://evidence.laceproject.eu/

5. Visualising evidence
http://evidence.laceproject.eu/

6. You can help
http://evidence.laceproject.eu/contribute/add-evidence/

7. • Rebecca Ferguson, The Open University, UK
Introduction
• Alejandra Martínez Monés, University of Valladolid, Spain
Building on past collaborations
• Kairit Tammets, Tallinn University, Estonia
Work in Estonia
• Alan Berg, University of Amsterdam, The Netherlands
Work in The Netherlands
• Anne Boyer, Université de Lorraine, France
Work in France
• Hendrik Drachsler (OUNL) and Adam Cooper (Cetis), LACE
Moving forward together – the LACE project
• Questions and discussion

8. Learning Analytics: European
Perspectives.
Kaleidoscope NoE
Alejandra Martínez Monés
18-3-2015

9. Was there learning
analytics before “learning
analytics”?
LAK 15 Poughkeepsie, March 11, 2015

10. Let’s make a trip,
Geographically … … and in time
2004-2007 2009-2011
Learning
Analytics
Kaleidoscope
NoE
LAK 15 Poughkeepsie, March 11, 2015

11. Kaleidoscope NoE
• Projects related to Computer-Assisted
Interaction Analysis
– ICALTS & IA (2004-2005)
Interaction and Collaboration Analysis Supporting
Teachers’ and Students’ Self-Regulation
– CAVicola (2006)
Computer-based analysis and visualization of
Collaborative Learning activities
LAK 15 Poughkeepsie, March 11, 2015

12. Who?
• Researchers
– From the AIED and CSCL research communities
– Different backgrounds (Psychology, Computer Science …)
• Interest in using data provided by computer logs
to understand (collaborative) learning and help
teachers and learners improve the learning
process
LAK 15 Poughkeepsie, March 11, 2015

13. WE HAD A SMALL IMPACT
LAK 15 Poughkeepsie, March 11, 2015

14. Small impact
• A small number of researchers, coming from
the “CS” in CSCL
• Focus on understanding learning and on
providing support on a small scale
• Unstable prototypes with little usability
LAK 15 Poughkeepsie, March 11, 2015

15. Small impact (cont)
• We had no “name”, no “label” for what we
were doing:
• (Computer-supported) Interaction / Collaboration
Analysis
• Collaborative learning modeling
• Management of collaborative interaction
• …
LAK 15 Poughkeepsie, March 11, 2015

16. BUT WE HAD WORKED HARD AND
ACHIEVED SOME OUTCOMES
LAK 15 Poughkeepsie, March 11, 2015

17. Main outcomes
1. Conceptualization of
analysis indicators and
methods
• A catalogue of indicators
• A model of computer
supported interaction analysis
process
LAK 15 Poughkeepsie, March 11, 2015

18. Main outcomes
2. Focus on interoperability
– A common data format to
enable data exchange
between learning tools
and analysis tools
– Cross-case studies with
participation of all the
partners
LAK 15 Poughkeepsie, March 11, 2015

19. Main outcomes
3. Focus on visualization
of the analysis
– Augmentation of Social
Networks with:
• measurements and
properties
• navigability
LAK 15 Poughkeepsie, March 11, 2015

20. Was there learning analytics
before “learning analytics”?
LAK 15 Poughkeepsie, March 11, 2015

21. Recommended readings
• Harrer, A., Martínez Monés, A., Dimitracopoulou, A. Users’ data:
Collaborative and social analysis in Technology-Enhanced Learning:
Principles and Products, Springer, Netherlands, 2009.
• Nicolas Balacheff, Kristine Lund. Multidisciplinarity vs. Multivocality, the
case of "Learning Analytics”. LAK 2013 - International Conference on
Learning Analytics and Knowledge, Apr 2013, Leuven, Belgium. ACM New
York, NY, USA, pp.5-13,
LAK 15 Poughkeepsie, March 11, 2015

22. Learning Analytics:
European Perspectives
Estonian context
Kairit Tammets
Tallinn University
18. March 2015

23. Learning Analytics in Estonia
• Two state level initiatives:
– Educational resources cloud for secondary education
– eDidaktikum for teacher education
• Few research-based software development initiatives:
– Dippler
– Edufeedr
– ....
• Several EU funded projects with LA element:
– EMMA - European Multiple MOOC Aggregator
– Learning Layers – Scaling Up Technologies for Informal Learning in SME
Clusters
– WatchMe

24. eDidaktikum
• … is a learning environment for Estonian teacher education
• … partnership between five teacher education institutions
• … aims to provide knowledge construction and sharing
across the borders of educational institutions in pre-service
context
• … enhances the development of informal and formal
professional communities for teachers
• … supports competency-based learning

25. Learning Analytics in eDidaktikum (1)
• Instant feedback through dashboard to:
– Learners:
• Overall progress in the course based on
assignments, accessed materials, … ;
• Emerged networks of students and artefacts
based on comments in weblog, replies in forum,
accessed materials;
• Competency profile based on evidences in the
system

26. Learning Analytics in eDidaktikum (2)
• Instant feedback through dashboard to:
– Course designers:
• Students in fall-out position in formal courses;
• Most used and less used learning resources;
• Overview of task performance;
• Emerged networks of resources and learners;
• Competency profile of course based on evidences in
the system

27. Learning Analytics in eDidaktikum (3)
• Retrospective analysis in progress:
– What pedagogical patterns emerge in the eDidaktikum:
• Knowledge building – discussions, comments,
collaborative work
• Knowledge testing – mainly performing assignments;
• Knowledge storing and distributing – mainly using
eDidaktikum as repository;

28. Technical architecture of eD LA
• Drupal-based eDidaktikum;
• Open-source Learning Record Store Learning
Locker;
• xAPI statements between eDidaktikum and
Learning Locker
• Highcharts for visualizations on dashboards

30. Educational Cloud
• Cloud makes accessible for end-users (teachers, students
and parents) stored (non-)commercial resources in the
publishers’ servers, learning resources repositories and
different web services (Youtube, Slideshare, Flickr
LearningApps)
• End-users may create collections of (non-)commercial
resources, re-use and share them
• The system tracks interactions with these collections and
related resources and aggregated interaction data will be
collected in a learning record store.

31. Learning Analytics in Educational Cloud
LA dashboards for:
• Teachers and students:
• Recommendations about resources to use in collection
based on subject, history of browsing collections and
materials, used resources;
• Overview of collections: nr of accessing, re-using,
commenting collection.

32. Learning Analytics in Educational Cloud
• LA dashboards for:
– Representative of the repository (publishers,
existing digital learning resource repositories):
• Usage of the resources in different schools: most
and least used resources, number of accessing
and using of the materials;
• Overall overview of usage of resources in
collections created by students and teachers

33. Dippler
• … pedagogy-driven software development project (2011 -
2013) funded by Estonian Information Technology Foundation
for Education;
• … is a digital learning ecosystem intended for use in higher
education;
• … supports: self-directed learning, competence-based
education, collaborative knowledge building, task-centered
instructional design

34. Learning Analytics in Dippler
• Adapted activity stream: pedagogic vocabulary added to
actors, objects, verbs
• Linking events and learning resources with tasks and learning
outcomes
• Adding semantics through domain ontology keywords
(taxonomy) and user-defined tags (folksonomy)
• Using native features of Wordpress: categories and tags

36. Conclusion
• Learning Analytics is new for our educational sector and so far interest
object of small research group and EU funded projects
• Preparing the collaboration between Estonian and Finnish joint
educational cloud;
• Recently funded Era-Chair proposal “Cross-Border Educational
Innovation thru Technology-Enhanced Research”, aims to increase
TLU capacity in research based teacher education especially focusing
on Learning Analytics tools and methodologies;
• Plan to establish Estonian Learning Analytics SIG to involve
researchers of our universities, policy makers and industries

37. Used materials
• Eradze, M., Laanpere M. (2013). Analysing Learning Interactions in
Digital Learning Ecosystems based on Learning Activity Streams.
http://www.slideshare.net/martlaa/ecer13-learning-interactions
• Põldoja, H. (2013). Dippler and EduFeedr: two approaches to blog-
based course. http://www.slideshare.net/hanspoldoja/2013-1004-
dippler-edu-feedr

38. THANK YOU!
PhD Kairit Tammets
Centre for Educational Technology
Tallinn University
Estonia
Contact: kairit@tlu.ee

39. Learning Analytics
NL
OR oh dear how much time do I have?

40. Who am I
● Board of Directors Apereo Foundation
● Community officer Apereo LAI
● Co-Chair SIG LA SURF
● Program manager Learning Analytics UvA
● Innovation Work Group
● I like meeting new people.
● TALK TO ME :)

41. Special Interest Group Learning Analytics
https://www.surf.nl/diensten-en-producten/communitys-special-interest-groups-sigs/index.html
https://www.surfspace.nl/sig/18-learning-analytics/
S
U
R
F
https://www.surf.nl

42. Joining of the dots in the ecosphere
from the NL perspective
SOLAR
LACE
JISC
Apereo
Foundation
Universities
SURF
SIG LA
Commercial
Companies
ESUP
Schools
Kennisnet
CJKR
Grant bodies
Standards bodies
EU
Consortiums
New Faces

43. The success of the SIG LA can be
measured by SNA.
Picture taken from a slide by Jerone Donkers

44. Hackathons to share requirements
● How many Universities have built dashboards?
● How many have built them for the wrong requirements
● SURF / UvA / Apereo hackathon
● LAK15 (SOLAR, UvA, Apereo, Unicon, NWU)
o Share experience
o Share infrastructure
o Share requirements
o Share artifacts
o Meet new people
● Ethics and privacy workshops → LACE

46. (LA is Nicely bubbling)
● Eduworks (EU consortium)
● Apereo LAI
o OAAI, Open DashBoard, LRS
o LTI, PMML xAPI (more standards please).
● Learning Record Store
● xAPI (Unicon) enabling Sakai,uPortal, Apereo OAE
● POC’s - COACH, UvAnalytics
● Hackathon, workshops.
● Focus group LA - Stefan Mol Chair
● UvAInform: Stimulation grant for 7 pilots

47. UvAInform
● Establish needs across the UvA Community
● Establish priorities, synergies and economy of scale
● Gain Experience with LA Infrastructure
● Generate evidence
By pilots at Facilities ± 1000 students

48. UvAInform
❏ Clustered Exam Feedback
❏ COACH2 (Group mirroring)
❏ Effective Comparative Feedback
❏ Goal Setting platform
❏ Validating LA in Higher ED
❏ Assignment Criteria Validation
❏ Web Lecture Statistics
❏ Learning Record Store
❏ Open Dashboard ??

49. Scaling up obstacle for 2016
Data centralism and actions generated from the data
motivates the need for a central data
governance and innovation body.
UvA then NL?

50. Learning Analytics:
European Perspectives
Work in France
Anne Boyer

51. A short snapshot
• a community under construction
– several laboratories working in LA
• some national actions
– an on-going survey, with a cartography of research activity
– a workgroup about data provision to research teams & ethical
questions
– link with practitioners (as Esup Portail consortium)
• some national research projects
– PIA 1 e-education Péricles
– ANR Hubble

52. The PIA 1 Péricles project
• topic: quality assessment in education
• supported by "Investment for the Future”
(PIA) e-education project
• beginning: November 2012
• End: May 2016
• http://www.e-pericles.org

53. Administrative description
• coordinator: Jacques Dang (HEC) & a company ALTRAN
• partners:
– 1 research team
• KIWI team – LORIA lab, Université de Lorraine
– 2 digital thematic universities
• Université Ouverte des Humanités
• AUNEGE
– 4 companies:
• e-charlemagne
• Altran
• Sailendra SAS
• DEMOS France
– and many associated partners

54. Main objectives
• Design and experimentation of an open
source tool
– dedicated to HE institutions
– to run an quality process based on criteria
selected in a public databasis or internally defined
• Personalized recommendations of learning
resources or formation program in LLL

55. Scientific objectives
• Collection and exploitation of digital traces let
by learners when interacting with a repository
of OERs or with a pedagogical platform
• Design of hybrid recommenders, depending of
the available data

56. The Hubble project
• HUman oBservatory Based on anaLysis of e-
LEarning traces
• supported by the French national agency for
research (ANR)
• beginning: sept. 2014
• http://www.agence-nationale-recherche.fr/projet-
anr/?tx_lwmsuivibilan_pi2%5BCODE%5D=ANR-14-CE24-0015

57. Administrative description
• coordinator: Venda Luengo (Laboratory LIG, Grenoble)
• partners:
– 7 laboratories or research teams:
• Equipe MeTAH, Laboratory LIG, Université Grenoble Alpes
• Laboratoire LINA, Université de Nantes
• Equipe Silex, Université Claude Bernard, Lyon 1
• Equipe IEIAH, Université du Mans (MAINE)
• Equipe EduTICE, Ecole normale supérieure de Lyon
• Laboratoire STEF, Ecole normale supérieure de Cachan
• Laboratoire LabSTICC, Institut de Mines Télécom, Télécom
Bretagne
– 1 company: Entreprise OpenClassrooms

58. Main objectives
• creation of an observatory for the
construction and the sharing of massive data
in e-learning, of their analysis process and
their usage context
• creation of a community on Learning Analytics

59. Scientific objectives
• Proposition of models, languages &methods
to support all users (mostly not computer
scientists) in the interpretation of massive
traces
– traces collection and modeling
– tools to analyze traces
– means to describe analysis process for various
stakeholders

60. As a conclusion
• About 10 labs or teams working on Learning
Analytics
• An interdisciplinary community under
construction

61. The Why and How of LACE
LAK15 Panel March 18 2015
Learning Analytics European Perspectives
#laceproject
Adam Cooper
Cetis

62. ... because it is hard to get to the top alone

63. Learning Analytics in Europe –
some of the issues

64. Union Does Not Mean Uniformity
Politically
• Lisbon Treaty
“supporting, coordinating or
complementary actions”
• Harmonisation NOT permitted
Culturally
• Pedagogy
• Organisations
• Authority and freedom
• Privacy

65. OptimisingSuccess?

66. Wehavesomeparadoxes
todealwith
66

67. • Research
• Suppliers
• IT
• Policy
67
Silos Do Matter

68. Size Does Matter
• Too small and too big
• Fragmented
– Research
– Ed Tech market
68

69. So... what are we doing?
69

70. Vision
Building bridges between research, policy and
practice to realise the potential of learning
analytics in EU.

71. Getting People Talking
• Ethics and privacy
workshops
• Bett
• EC-TEL, LAK
• EUNIS
• European Parliament

72. Capture, write,
share, sense-make
LACE website
Evidence Hub Guidance and White Papers

73. Modularity, Standards, Shared Infrastructure
Contribute to the
Open Learning Analytics Network
1. Open Data and Models
2. Open Research
3. Open-Source
Software/Platforms
4. Open Strategy and Policy
5. Open Learning Designs

74. Building a Mutually-supportive Network
74
LACE Network
LACE Consortium

75. We Work With Associated Partners To...
and to:
• provide mutual support in out-reach
and dissemination;
• co-organise events;
• co-author reports and provide peer-
review;
• help to join-up communities of
educators, researchers, policy-makers,
and suppliers;
• discuss emerging themes and priorities
for action;
• avoid duplication of effort and maximise
synergy.
run sessions like this
(thanks panel members!)

76. Join with us!

77. Credits
Eiger north face CC BY-SA 3.0 Terra3
http://en.wikipedia.org/wiki/Eiger#/media/File:North_face.jpg
Map of countries in Europe CC BY-SA 3..0 San Jose
http://commons.wikimedia.org/wiki/File:Europe_countries_map_en.png
Superior Portland Cement Silos - Concrete Washington - in Autumn CC BY-SA 3.0 SkagitRiverQueen
http://en.wikipedia.org/wiki/Concrete,_Washington#/media/File:Concrete_silos_in_autumn.jpg
A monocultivated potato field CC BY 2.0 NightThree
http://en.wikipedia.org/wiki/Monoculture#/media/File:Tractors_in_Potato_Field.jpg
Resist Monoculture, reclaim the commons CC BY-ND Sasha Y. Kimel
https://www.flickr.com/photos/sashakimel/8737861544/
Learning Analytics Diamond CC BY-NC-SA Aaron Zeckoski, Unicon
And with thanks to Hendrik Drachsler, OUNL, from whom I borrowed some slides and Maren Scheffel,
OUNL, who took some of the LACE event photographs.

78. “The Why and How of LACE” by Adam Cooper, Cetis, was presented at the
LAK15 Panel “Learning Analytics European Perspectives” on March 18 2015.
adam@cetis.org.uk
The LACE Project is 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