Live Video Analysis for Enhanced Educational Content Evaluation

Abstract Digital learning platforms have grown rapidly and changed the face of traditional education to an interactive and animated one. Nevertheless, measuring educational content effectiveness and engagement level continues to be a difficult task. The project, Live Video Analysis for Enhanced Educational Content Evaluation, proposes building a smart system that can analyze the live video streaming of learners and evaluate the content impact materials and methods. By merging live video analytics with educational assessment, this project aims to raise the quality of e-learning environments and make the learning experience more personalized and adaptive. Not only does this approach increase learner engagement, but it also provides priceless information for the ongoing improvement of digital education instantaneously. The system uses computer vision and machine learning techniques to recognize facial expressions, eye movements, and body postures. From these, the learner's attention, comprehension, and emotional reactions during the learning sessions can be inferred. The gathered data are then processed to create feedback reports that enable teachers to adjust their teaching Key Words - Student recognition, student activities monitoring, deep learning, engagement detection, digital classroom, e-class.