Recently, multiple applications of machine learning have been introduced. They include various possibilities arising when image analysis methods are applied to, broadly understood, video streams. In this context, a novel tool, developed for academic educators to enhance the teaching process by automating, summarizing, and offering prompt feedback on conducting lectures, has been developed. The implemented prototype utilizes machine learning-based techniques to recognise selected didactic and behavioural teachers' features within lecture video recordings. Specifically, users (teachers) can upload their lecture videos, which are preprocessed and analysed using machine learning models. Next, users can view summaries of recognized didactic features through interactive charts and tables. Additionally, stored ML-based prediction results support comparisons between lectures based on their didactic content. In the developed application text-based models trained on lecture transcriptions, with enhancements to the transcription quality, by adopting an automatic speech recognition solution are applied. Furthermore, the system offers flexibility for (future) integration of new/additional machine-learning models and software modules for image and video analysis.

翻译：近年来，机器学习技术已实现多领域应用。其中，将图像分析方法应用于广义视频流处理展现出广阔前景。在此背景下，我们开发了一款面向高校教育工作者的新型工具，该工具通过自动化处理、内容摘要及即时反馈功能来优化教学过程。所实现的原型系统采用基于机器学习的技术，用于识别教学视频中特定的教学行为特征与教师行为模式。具体而言，用户（教师）可上传授课视频，系统通过机器学习模型进行预处理与分析。随后，用户可通过交互式图表查看已识别教学特征的摘要报告。此外，系统存储的机器学习预测结果支持基于教学内容的跨课程对比分析。本应用采用基于文本的模型，通过集成自动语音识别技术提升转录质量，并利用授课转录文本进行训练。该系统还具备良好扩展性，可支持（未来）集成新增的图像/视频分析机器学习模型与软件模块。