learning analytics for educational design and student predictions: beyond the hype with real-life...
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Learning Analytics for Educational Design and Student Predictions:Beyond the Hype with Real-Life Examples
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Learning Analytics for Educational Design andStudent Predictions
Nynke Kruiderink – University of Amsterdam
Nynke Bos – University of Amsterdam
Perry J. Samson – University of Michigan- Ann Arbor
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Who we are
Nynke BosHead of ICT, Faculty of Humanities
Nynke KruiderinkTeamleader Educational Technology of Social Sciences, Faculty of Social and Behavioral Sciences
University of Amsterdam, The Netherlands30,000 students
5000 employees
annual budget 600 Million euro’s (810 Million dollars)
57 bachelor’s programmes
92 masters’s programmes
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Lessons Learned Feb 2012-present
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Proof of Concept
Two tiered:Interviews with lecturers, professors, managersGather and store data in central place for easy access
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Lessons Learned
1. Emotional response to ‘Big Brother' aspect of accessing data
2. Data from LMS not detailed enough (folder based not file based)
3. 50% of learning data available
4. Piwki, not secure enough
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
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
Student vs. Policy
Students ‘demanded’ policy
Quality assurance department wanted insight into academic achievement before doing so
Development of didactic framework
Research: Learning Analytics
Design
Two courses on psychology
Courses run simultaneously
Intervention in one condition, but not in the other
A thank you
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
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
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LectureTools: Student View
LectureTools: Responder
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LectureTools: Questions
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LectureTools: Analytics
LectureTools: Analytics
LectureTools: Analytics
LectureTools: Analytics
LectureTools: Analytics
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