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Detecting pupils’ opinions on learning physics bodily by unsupervised machine learning
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, , , University of Jyväskylä, Finland

AACE Award

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Las Vegas, NV, United States ISBN 978-1-939797-35-3 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA

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Abstract

The purpose of this study is to explore the added value that bodily learning brings to the study of physics at lower secondary school and how pupils experience a new way of studying physics. The study compares how pupils who liked and disliked physics experienced the new teaching methods. Here we use unsupervised machine learning in order to discover new information from the inquiry, that was held after the workshop, where pupils were experimenting with new ways of studying physics. We find that unsupervised machine learning can be a helpful tool for teachers to detect students preferred learning styles and different types of personalities in the classroom.

Citation

Moilanen, H., Äyrämö, S. & Kankaanranta, M. (2018). Detecting pupils’ opinions on learning physics bodily by unsupervised machine learning. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 27-38). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved January 27, 2021 from .

© 2018 Association for the Advancement of Computing in Education (AACE)

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