Amsterdam University of Applied Sciences (NETHERLANDS)
About this paper:
Appears in: EDULEARN13 Proceedings
Publication year: 2013
Pages: 3209-3214
ISBN: 978-84-616-3822-2
ISSN: 2340-1117
Conference name: 5th International Conference on Education and New Learning Technologies
Dates: 1-3 July, 2013
Location: Barcelona, Spain
Learning Tomorrow is a program at the Amsterdam University of Applied Sciences that stimulates the use of digital tools to enable a more personal, informal and collaborative way of working within the university. In order to optimize this and gain a better understanding of challenges and opportunities, it is vital to visualize the process. A study was conducted on the current state of education. How do students and staff enable their personal study and working style?

With the use of QS (Quantified Self) technologies 25 students and staff were tracked for a minimum of two weeks. With GPS trackers it was possible to see geospatial patterns that translated into classrooms without walls and digital communication services. With the use of Fitbits (digital, auto synchronizing pedometers), sleep patterns and work/study activities were uncovered. In parallel, participants shared their daily activities in a Facebook group and were given a questionnaire to explain how they work and study.

The results reveal how students currently create their personal learning style and how teachers work with students, digitally. In terms of sleep patterns, the study showed that students got significantly more sleep than staff members. An important finding was that students and staff members indicated that their favorite place to work and/or study was not located on the university campus.

The raw data was analyzed and visualized by ten Communication and Multimedia Design students from different years, guided by two data visualization and QS experts. This resulted in seven projects from students, for students, about students. For example, a system that provided students with advises for an optimal location to live (and lose less time traveling), resulting from the analysis of distances that were travelled.