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Join the presentation with a demonstration of the LectureTools account at my.lecturetools.com. Login with the email "demo2721" (no password required) and explore the use of learning analytics in educational design and student predictions. Presented by Nynke Kruiderink, Nynke Bos, and Perry J. Samson from University of Amsterdam and University of Michigan-Ann Arbor.
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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.
my.lecturetools.com :: user = demo2721 (no password needed) Nynke Kruiderink – University of Amsterdam Nynke Bos – University of Amsterdam Perry J. Samson – University of Michigan- Ann Arbor Learning Analytics for Educational Design andStudent Predictions
my.lecturetools.com :: user = demo2721 (no password needed) 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) 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 easy access
Lessons Learned • 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
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
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)
