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.

翻译：本文旨在开发并评估一个集成系统，该系统能够自动评估授课风格，使教师能够即时获得关于其授课风格质量的反馈。该系统旨在促进授课质量的提升，进而优化学生的整体学习体验。该应用利用从观众视角录制的讲师视频中提取的可测量生物特征，例如面部表情、身体活动、语速与语调、手势以及面部姿态。在授课过程中提取的可测量生物特征被整合，为教师提供反映授课风格质量的评分，既涵盖逐帧评分，也包括整个授课过程的授课质量指标。本研究通过首席教育官员、教师及学生对所提出授课风格评估系统的功能、应用实用性及潜在改进方向进行了评估。结果表明，参与者认为该应用在提供授课质量自动化反馈方面具有新颖性和实用性。此外，将所提出系统的性能与人类在授课风格评估任务中的表现进行了对比。结果表明，所提出的系统不仅达到了与人类观察者相当的性能，在某些情况下甚至优于人类。