An Analysis of the Construction Methods of Multimodal Course Knowledge Graphs

Abstract

In the context of digitalization, course resources exhibit multimodal characteristics, covering various forms such as text, images, and videos. Course knowledge and learning resources are becoming increasingly diverse, providing favorable conditions for students’ in-depth and efficient learning. Against this backdrop, how to scientifically apply emerging technologies to automatically collect, process, and integrate digital learning resources such as voices, videos, and courseware texts, and better innovate the organization and presentation forms of course knowledge has become an important development direction for “artificial intelligence + education.” This article elaborates on the elements and characteristics of knowledge graphs, analyzes the construction steps of knowledge graphs, and explores the construction methods of multimodal course knowledge graphs from aspects such as dataset collection, course knowledge ontology identification, knowledge discovery, and association, providing references for the intelligent application of online open courses.