Year: 2024
Type: conference-paper
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.
Authors: Linlin Zhao, Zhansheng Liu, Xuefeng Zhao
Institution: Beijing University of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Beijing University of Technology
OpenAlex ID: W4409965713