Developing a Knowledge Graph for Intelligent Structural Design Course

Knowledge graphs provide concept visualization and context information across many applications. However, the process of building a knowledge graph by transforming large and intricate unstructured data into a new domain presents numerous challenges. Learning from an Intelligent Structural Design (ISD) course requires comprehension of structural concepts, design techniques, AI technologies, and the tackling of multifaceted real-project problems, which require adaptive learning strategies and cognitive engagement. This study introduces a bottom-up approach to constructing a knowledge graph specifically designed for the course. Furthermore, by employing a deep learning model, a personalized learning path could be generated based on a student's submitted assignments. To evaluate the effectiveness of this novel educational paradigm, an experiment was conducted involving two groups. The results indicate that participants who used the Course Knowledge Graph (CKG) attained higher scores than those instructed through conventional teaching methods.

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Publication Details

Published
2024-11-22
DOI
https://doi.org/10.1145/3722237.3722259
Citations
1
Primary Topic
Advanced Data Processing Techniques
Type
article
Field-Weighted Citation Impact
0.33
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article

Developing a Knowledge Graph for Intelligent Structural Design Course

Xuefeng Zhao, Linlin Zhao, Zhansheng Liu
1 citations
Advanced Data Processing Techniques
0.33
article

Developing a Knowledge Graph for Intelligent Structural Design Course

Xuefeng Zhao, Linlin Zhao, Zhansheng Liu
article en
1 citations

Abstract

Knowledge graphs provide concept visualization and context information across many applications. However, the process of building a knowledge graph by transforming large and intricate unstructured data into a new domain presents numerous challenges. Learning from an Intelligent Structural Design (ISD) course requires comprehension of structural concepts, design techniques, AI technologies, and the tackling of multifaceted real-project problems, which require adaptive learning strategies and cognitive engagement. This study introduces a bottom-up approach to constructing a knowledge graph specifically designed for the course. Furthermore, by employing a deep learning model, a personalized learning path could be generated based on a student's submitted assignments. To evaluate the effectiveness of this novel educational paradigm, an experiment was conducted involving two groups. The results indicate that participants who used the Course Knowledge Graph (CKG) attained higher scores than those instructed through conventional teaching methods.

Beijing University of Technology (CN), Beijing University of Civil Engineering and Architecture (CN)
Openalex Percentile: Top 35%
Advanced Data Processing Techniques
0.33
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