Dynamic Generation of Knowledge Graph Supporting STEAM Learning Theme Design
Source abstract
Abstract
Instructional framework based on a knowledge graph makes up for the interdisciplinary theme design ability of teachers in a single discipline, to some extent, and provides a curriculum-oriented theme generation path for STEAM instructional design. This study proposed a dynamic completion model of a knowledge graph based on the subject semantic tensor decomposition. This model can be based on the tensor calculation of multi-disciplinary curriculum standard knowledge semantics to provide more reasonable STEAM project-based learning themes for teachers of those subjects. First, the STEAM multi-disciplinary knowledge semantic dataset was generated through the course’s standard text and open-source encyclopedia data. Next, based on the semantic tensor decomposition of specific STEAM topics, the dynamic generation of knowledge graphs was realized, providing interdisciplinary STEAM learning topic sequences for teachers of a single discipline. Finally, the application experiment of generating STEAM learning themes proved the effectiveness of our model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Advanced Graph Neural Networks · Education and Vocational Training
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Educational Information Technology, South China Normal University, Guangzhou 510631, China
School of Computer Science and Intelligence Education, Lingnan Normal University, Zhanjiang 524048, China
Funding
National Key R&D Program of China · Guangdong Provincial Philosophy and Social Science Foundation
References (32)
Show source-provided references
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