Learning Analytics for Educational
Design and Student Predictions:
Beyond the Hype with Real-Life
Examples
Join presentati...
Nynke Kruiderink – University of Amsterdam
Nynke Bos – University of Amsterdam
Perry J. Samson – University of Michigan- A...
Who we are
Nynke Bos

Head of ICT, Faculty of Humanities

Nynke Kruiderink

Teamleader Educational Technology of Social Sc...
Lessons Learned
Feb 2012-present

my.lecturetools.com :: user = demo2721 (no password needed)
Proof of Concept
Two tiered:
Interviews with lecturers, professors, managers
Gather and store data in central place for ...
Lessons Learned
1.

2.

3.
4.

Emotional response to ‘Big Brother' aspect of
accessing data
Data from LMS not detailed eno...
Next steps


Focus group Learning Analytics



Professor Erik Duval – KU Leuven
What is the problem?


Recorded lectures
 Recording of face-to-face lectures


No policy at the University of Amsterdam...
Student vs. Policy


Students ‘demanded’ policy



Quality assurance department wanted insight into
academic achievement...
Design


Two courses on psychology



Courses run simultaneously



Intervention in one condition, but not in the other...
Data collection




Viewing of recorded lecture
Lecture attendance per lecture
Final grade on the course
 more segment...
Lessons Learned
Let people know what you are doing
 Data preparation: fuzzy, messy
 Choose the data


Simplify the data...
LectureTools: Student View

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

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

my.lecturetools.com :: e-mail = “demo2721” (no password)
LectureTools: Analytics
LectureTools: Analytics
LectureTools: Analytics
LectureTools: Analytics
LectureTools: Analytics
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Learning Analytics for Educational Design and Student Predictions: Beyond the Hype with Real-Life Examples

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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).

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Learning Analytics for Educational Design and Student Predictions: Beyond the Hype with Real-Life Examples

  1. 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. 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. 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. 4. Lessons Learned Feb 2012-present my.lecturetools.com :: user = demo2721 (no password needed)
  5. 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. 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. 7. Next steps  Focus group Learning Analytics  Professor Erik Duval – KU Leuven
  8. 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. 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. 10. Design  Two courses on psychology  Courses run simultaneously  Intervention in one condition, but not in the other  A thank you
  11. 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. 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. 13. LectureTools: Student View my.lecturetools.com :: e-mail = “demo2721” (no password)
  14. 14. LectureTools: Responder my.lecturetools.com :: e-mail = “demo2721” (no password)
  15. 15. LectureTools: Questions my.lecturetools.com :: e-mail = “demo2721” (no password)
  16. 16. LectureTools: Analytics
  17. 17. LectureTools: Analytics
  18. 18. LectureTools: Analytics
  19. 19. LectureTools: Analytics
  20. 20. LectureTools: Analytics

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