Collecting and Using Students’ Digital Well-Being Data in Multidisciplinary Teaching
Moilanen, H., Äyrämö, S., Jauhiainen, S., & Kankaanranta, M. (2018). Collecting and Using Students’ Digital Well-Being Data in Multidisciplinary Teaching. Education Research International, 2018, Article 3012079. https://doi.org/10.1155/2018/3012079
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Education Research InternationalDate
2018Copyright
© 2018 Hannu Moilanen et al.
*is article examines how students (N � 198; aged 13 to 17) experienced the new methods for sensor-based learning in
multidisciplinary teaching in lower and upper secondary education that combine the use of new sensor technology and learning
from self-produced well-being data. *e aim was to explore how students perceived new methods from the point of view of their
learning and did the teaching methods provide new information that could promote their own well-being. We also aimed to find
out how to collect digital well-being data from a large number of students and how the collected big data set can be utilized to
predict school success from the students’ well-being data by using machine learning methods (lasso regression and multilayer
perceptron). Results showed that sensor-based learning can promote students’ learning and well-being. All upper secondary
school (n � 37) and 87% of lower secondary school pupils (n � 161) argued that when data are produced by their bodies, learning
is more interesting, and they mostly found that well-being analysis was useful (upper secondary 97%; lower secondary 78%) and
can improve personal well-being (upper secondary 78%; lower secondary 67%). *e predictive powers with lasso regression and
multilayer perceptron (MLP) were quite weak (correlation: −0.14 and 0.34, respectively).
...
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Hindawi Publishing CorporationISSN Search the Publication Forum
2090-4002Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/28714678
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