Detecting pupils’ opinions on learning physics bodily by unsupervised machine learning
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.
Presider: Anne Montgomery, University of Phoenix