Dinsdag 8 november Sessieronde 4 Titel: Learning analytics bij de Universiteit Utrecht Spreker(s): Johan Jeuring (Universiteit Utrecht) Zaal: Mees

1. Learning analytics
at
Utrecht University
Johan Jeuring
Utrecht University
De Onderwijsdagen
November 8, 2016

2. Learning analytics is 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.
Call for Papers of the 1st International Conference on Learning Analytics & Knowledge (LAK 2011)

4. Optimizing learning
Studie-adviseur
Teacher
System/student

5. And not
Students work hard before a deadline
Students look most at videos about difficult topics

6. Powell, Stephen, and Sheila MacNeill. Institutional Readiness for Analytics A Briefing Paper. CETIS Analytics Series. JISC
1. for individual learners to reflect on their achievements and
patterns of behaviour in relation to others
2. as predictors of students requiring extra support and attention
3. to help teachers and support staff plan supporting interventions
with individuals and groups
4. for functional groups such as course team seeking to improve
current courses or develop new curriculum offerings
5. for institutional administrators taking decisions on matters such
as marketing and recruitment or efficiency and effectiveness
measures

7. What do we do with learning analytics at
Utrecht University?

8. A lot!

9. • Blended learning and learning
analytics project within
Educate-IT
• Portfolio in master veterinary science
• European WATCHME project on
workplace-based feedback and
assessment and professional
development by means of learning
analytics
• Many more experiments, PhD research, tooling (myMediasite,
Scalable learning, DWO, Communicate, FeedbackFruits, …)

10. Nothing

11. Utrecht university has an institutional policy for the role of ICT
in education (Educate-IT), but
there is no institutional infrastructure for learning analytics
(yet)

12. Ownership of data
Summative assessment data is
owned by the university, and may
be used for awarding grades,
assessment of programs
Other data from student activities is
owned by the student and the university,
and may be used for optimizing learning
and teaching
The data is not owned by the tool in
which students perform activities

13. Location of data
In the tools students and teachers use

14. Accessing the data
• Through the tool
• Calling
• Using an API

15. Opinion 1
Don’t use a single tool (Blackboard, Osiris, myMediasite) for
learning analytics across an institution

16. Opinion 2
All software used in education should provide access to
student activity data through an API

17. A learning analytics service
• Collect student activities
• Facilitate creating a student / knowledge model
• Map activities on knowledge model
• Visualize knowledge / progress for students and
teachers

19. Evolution and biodiversity
4. Wat zijn de belangrijkste veranderingen die
geleidelijk in de vinkenpopulatie zullen plaatsvinden?
a. De eigenschappen van elke vink binnen een
populatie zal geleidelijk veranderen.
b. Het aandeel van vinken binnen de populatie die
andere eigenschappen hebben, zal veranderen.
c. Succesvol gedrag dat de vinken hebben geleerd,
zal worden doorgegeven aan de nakomelingen.
d. Er zullen mutaties voorkomen, om aan de behoeften
van de vinken te voldoen tijdens de gewijzigde
omstandigheden.

20. Interpretation
A. Essentialistic (misconcept)
B. Correct
C. Anthropocentric (misconcept)
D. Teleologic (misconcept)

22. Technologies
• Learning Record Store
• xAPI
• Student model (but also group profile, and
predictions based on previous groups)
• Bindings with various tools: Remindo, Scalable
learning, Presenter’s wall, Communicate,
Traintool, myMediasite, …

23. Student activities

24. Learning record store

25. xAPI
RESTful web services
HTTP POST or GET statements, results are stored in an LRS

26. Recipes

27. Build (or buy) a separate learning analytics service/
infrastructure
Opinion 3

28. Opinion 4
For institutional learning analytics: use the rich data already
available: grades, assessment results, etc.
Don’t start with collecting data about students accessing on-
line material, watching video’s, etc.

29. Conclusion
• Develop an institutional policy for learning
analytics
• Watch out for tool-specific learning analytics
• Develop or buy a separate learning analytics
service and move software providers to provide
the relevant data
• Start with using data that is already there, and is
easy to interpret