Abstract:
This project aims to address the challenge that students and learners face in accessing videos that provide them with the right knowledge of the topic they want to learn, or the lecture notes uploaded by their instructors. Traditional video searching often recommends irrelevant videos and can waste valuable time for students. To overcome these limitations, this initiative introduced an automated video recommender that harnesses deep learning and machine learning technologies. The proposed system has been designed to analyze video content with high accuracy to enhance the precision of relevant video recommendations for educational purposes. The primary objective of this project is to revolutionize the search and recommendation of video content within a learning management system by developing a reliable, scalable, and user-friendly platform for students and teachers. By automating the video recommender, the system is not only time efficient but also helps the user obtain the most relevant and best video according to the topic they want to study and teach, making it an invaluable asset across learning management systems. A standout feature of this solution is its web-based interface, which facilitates easy interaction for students, learners, and teachers to obtain the automated link of their topic, and they can also obtain their previous links found in their history, which were saved in their databases. Ultimately, this project aims to significantly improve educational and learning outcomes for individuals by finding the most efficient and relevant YouTube videos. Using advanced technologies and educational expertise, this project aims to enhance the learning experience by democratizing access to relevant video content, streamlining the teaching process, and improving knowledge acquisition for students. This innovative approach marks a significant step towards major advancements in educational technology, offering enhanced experiences in the learning management system.
