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套集成式自动讲课风格评估系统，使教师能够即时获取关于其授课风格优劣的反馈。该系统旨在促进讲课质量提升，从而改善学生的整体学习体验。所提出的应用利用从观众视角录制的演讲者视频中提取的特定可测量生物特征，包括面部表情、身体活动、语速与语调、手势动作及面部姿态。讲课过程中提取的可测量生物特征被整合，为教师提供反映讲课风格质量的评分，该评分既包含逐帧指标，也涵盖整堂课的讲课质量度量。本研究由首席教育官员、教师及学生对所提讲课风格评估系统的接受度进行评价，重点关注应用的功能性、实用性及可能的改进空间。结果表明，参与者认为该应用在提供关于讲课质量的自动化反馈方面具有新颖性与实用性。此外，我们将所提系统的性能评估结果与人类在讲课风格评估任务中的表现进行了比较。结果显示，该系统不仅达到了与人类观察者相近的性能，在某些情况下甚至表现更优。