Abstract
Detecting the direction of the gaze and orientation of the body of both teacher and students is essential to estimate who is paying attention to whom. It also provides vital clues for understanding their unconscious, non-verbal behavior. These are called “honest signals” since they are unconscious subtle patterns in our interaction with other people that help reveal the focus of our attention. Inside the classroom, they provide important clues about teaching practices and students' responses to different conscious and unconscious teaching strategies. Scanning this non-verbal behavior in the classroom can provide important feedback to the teacher in order for them to improve their teaching practices. This type of analysis usually requires sophisticated eye-tracking equipment, motion sensors, or multiple cameras. However, for this to be a useful tool in the teacher's daily practice, an alternative must be found using only a smartphone. A smartphone is the only instrument that a teacher always has at their disposal and is nowadays considered truly ubiquitous. Our study looks at data from a group of first-grade classrooms. We show how video recordings on a teacher's smartphone can be used in order to estimate the direction of the teacher and students’ gaze, as well as their body orientation. Using the output from the OpenPose software, we run Machine Learning (ML) algorithms to train an estimator to recognize the direction of the students’ gaze and body orientation. We found that the level of accuracy achieved is comparable to that of human observers watching frames from the videos. The mean square errors (RMSE) of the predicted pitch and yaw angles for head and body directions are on average 11% lower than the RMSE between human annotators. However, our solution is much faster, avoids the tedium of doing it manually, and makes it possible to design solutions that give the teacher feedback as soon as they finish the class.
Highlights
Educational researchers have collected information on teacher and student classroom behavior for more than a century
Using Machine Learning (ML) we explore estimators that are robust to noisy data
In order to provide the teacher with a practical tool for analyzing the non-verbal behavior in their teaching practices, the proposed solution must ensure several conditions
Summary
Educational researchers have collected information on teacher and student classroom behavior for more than a century. In 1946, statistical information on different teacher practices in the classroom was collected via other methods such as filming individual teachers in action (National Education Association, 1946). This type of information is necessary for understanding the teaching practices that occur in the classroom. Transcriptions of slices from 710 videos of mathematics lessons taught by different teachers (Araya and Dartnell, 2008) revealed several insights, such as very little autonomous student participation, teachers neither presenting nor discussing any proofs, no use of information technology, almost no use of textbooks, and almost no explicit use of metaphors or analogies. The classifier trained with their data obtained a better level of agreement than the level of agreement between human raters (Araya et al, 2012)
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