SLIC: A System for Semantically Linked Instructional Content

Abstract

Video streaming is becoming a major channel for distance learning (or e-learning). A tremendous number of videos for educational purpose are captured and archived in various e-learning systems today throughout schools, corporations and over the Internet. However, making information searchable and browsable and presenting results optimally for a wide range of users and systems, thereby enriching learning experience remains a challenge. In this talk, I will present the SLIC (Semantically Linked Instructional Content) system for educational video browsing. The system aims to assist students and scholars to efficiently browse and seek segments of interest in educational videos. In particular, it focuses on lectures that use slides, where the content of the slides gives valuable hints as to how to break the video into meaningful parts (segments), and how to enable students to access these segments. Using similar ideas, the system has the potential to improve significantly the understandability of the video, enhance its quality, and increase the overall effectiveness of the learning process. During the talk, I will present the system, explain the core algorithms that were developed for the system, and demonstrate its usability.

Quanfu Fan is a fifth-year PhD student in the department of Computer Science at the University of Arizona. He has worked in the areas of computational geometry, image analysis and video analysis. Currently he focuses on developing a system for browsing educational videos.