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