  Understanding students' and teachers' verbal and non-verbal behaviours during
instruction may help infer valuable information regarding the quality of
teaching. In education research, there have been many studies that aim to
measure students' attentional focus on learning-related tasks: Based on
audio-visual recordings and manual or automated ratings of behaviours of
teachers and students. Student data is, however, highly sensitive. Therefore,
ensuring high standards of data protection and privacy has the utmost
importance in current practices. For example, in the context of teaching
management studies, data collection is carried out with the consent of pupils,
parents, teachers and school administrations. Nevertheless, there may often be
students whose data cannot be used for research purposes. Excluding these
students from the classroom is an unnatural intrusion into the organisation of
the classroom. A possible solution would be to request permission to record the
audio-visual recordings of all students (including those who do not voluntarily
participate in the study) and to anonymise their data. Yet, the manual
anonymisation of audio-visual data is very demanding. In this study, we examine
the use of artificial intelligence methods to automatically anonymise the
visual and audio data of a particular person.
