Spoken Knowledge Organization by Semantic Structuring and a Prototype Course Lecture System for Personalized Learning

摘要

It takes very long time to go through a complete online course. Without proper background, it is also difficult to understand retrieved spoken paragraphs. This paper therefore presents a new approach of spoken knowledge organization for course lectures for efficient personalized learning. Automatically extracted key terms are taken as the fundamental elements of the semantics of the course. Key term graph constructed by connecting related key terms forms the backbone of the global semantic structure. Audio/video signals are divided into multi-layer temporal structure including paragraphs, sections and chapters, each of which includes a summary as the local semantic structure. The interconnection between semantic structure and temporal structure together with spoken term detection jointly offer to the learners efficient ways to navigate across the course knowledge with personalized learning paths considering their personal interests, available time and background knowledge. A preliminary prototype system has also been successfully developed.