학술논문
Heterogeneous Evolution Network Embedding with Temporal Extension for Intelligent Tutoring Systems.
Document Type
Article
Author
Source
Subject
Language
ISSN
1046-8188
Abstract
The article introduces a novel approach, Heterogeneous Evolution Network Embedding with Temporal Extension (HEN-TE), for Intelligent Tutoring Systems (ITS). It addresses challenges in modeling ITS data, such as heterogeneity and dynamics. It is reported that HEN-TE incorporates a Heterogeneous Evolution Network (HEN) to represent entities and relations in ITS, and a Temporal Extension Graph Neural Network (TEGNN) to model evolving and static nodes.