  The aim of the work presented in this paper is to develop and evaluate an
integrated system that provides automated lecture style evaluation, allowing
teachers to get instant feedback related to the goodness of their lecturing
style. The proposed system aims to promote improvement of lecture quality, that
could upgrade the overall student learning experience. The proposed application
utilizes specific measurable biometric characteristics, such as facial
expressions, body activity, speech rate and intonation, hand movement, and
facial pose, extracted from a video showing the lecturer from the audience
point of view. Measurable biometric features extracted during a lecture are
combined to provide teachers with a score reflecting lecture style quality both
at frame rate and by providing lecture quality metrics for the whole lecture.
The acceptance of the proposed lecture style evaluation system was evaluated by
chief education officers, teachers and students regarding the functionality,
usefulness of the application, and possible improvements. The results indicate
that participants found the application novel and useful in providing automated
feedback regarding lecture quality. Furthermore, the performance evaluation of
the proposed system was compared with the performance of humans in the task of
lecture style evaluation. Results indicate that the proposed system not only
achieves similar performance to human observers, but in some cases, it
outperforms them.
