Interaction in this session will increase your insight into the tricky business of managing data. Subsequently, two examples will illustrate how learning analytics is being used to shape didactic frameworks and educational design (University of Amsterdam) and how it is being used to provide adaptive learning opportunities for students (University of Michigan).

1. Learning Analytics for Educational
Design and Student Predictions:
Beyond the Hype with Real-Life
Examples
Join presentation with “demo” LectureTools account:
•
Go to
http://my.lecturetools.com
•
Login with e-mail “demo2721”
(no password required)
•
Click
on subsequent page.

2. Nynke Kruiderink – University of Amsterdam
Nynke Bos – University of Amsterdam
Perry J. Samson – University of Michigan- Ann Arbor
Learning Analytics for Educational
Design and
Student Predictions
my.lecturetools.com :: user = demo2721 (no password needed)

3. Who we are
Nynke Bos
Head of ICT, Faculty of Humanities
Nynke Kruiderink
Teamleader Educational Technology of Social Sciences, Faculty of Social and Behavioral
Sciences
University of Amsterdam, The Netherlands
30,000 students
5000 employees
annual budget 600 Million euro’s (810 Million dollars)
57 bachelor’s programmes
92 masters’s programmes
my.lecturetools.com :: user = demo2721 (no password needed)

4. Lessons Learned
Feb 2012-present
my.lecturetools.com :: user = demo2721 (no password needed)

5. Proof of Concept
Two tiered:
Interviews with lecturers, professors, managers
Gather and store data in central place for easy
access
my.lecturetools.com :: user = demo2721 (no password needed)

6. Lessons Learned
1.
2.
3.
4.
Emotional response to ‘Big Brother' aspect of
accessing data
Data from LMS not detailed enough (folder
based not file based)
50% of learning data available
Piwki, not secure enough

7. Next steps

Focus group Learning Analytics

Professor Erik Duval – KU Leuven

8. What is the problem?

Recorded lectures
 Recording of face-to-face lectures

No policy at the University of Amsterdam

Different deployment throughout the curriculum
 Not
at all (fears/ emotional)
 Week
after the lecture
 Week
before the assessment
 And
all the scenario’s in between

9. Student vs. Policy

Students ‘demanded’ policy

Quality assurance department wanted insight into
academic achievement before doing so

Development of didactic framework

Research: Learning Analytics

10. Design

Two courses on psychology

Courses run simultaneously

Intervention in one condition, but not in the other

A thank you

11. Data collection



Viewing of recorded lecture
Lecture attendance per lecture
Final grade on the course
 more segmented view

Grades on previous courses

Distance to the lecture hall

Gender

Age

Hits in Blackboard

Inventory Learning Style (ILS: Vermunt, 1996)
Students were asked to fill out a consent form

12. Lessons Learned
Let people know what you are doing
 Data preparation: fuzzy, messy
 Choose the data

Simplify the data
 Keep an eye on the prize


13. LectureTools: Student View
my.lecturetools.com :: e-mail = “demo2721” (no password)

14. LectureTools: Responder
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15. LectureTools: Questions
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16. LectureTools: Analytics

17. LectureTools: Analytics

18. LectureTools: Analytics

19. LectureTools: Analytics

20. LectureTools: Analytics