Curriculum Content Construction and Updating for Intelligent Educational Systems Using Knowledge Graphs

Abstract In an intelligent education system, accurately updating and constructing course content helps to improve the intelligence of the system and the learning efficiency of students, so this paper constructs an intelligent education system using knowledge graph. The system accomplishes ontology construction, extraction, storage and intelligent question and answer of knowledge through knowledge graph technology to realize the integration of course content. The BI-LSTM-CRF model was used for entity naming of the system’s Q&A function, the Q&A was categorized on the basis of feature extraction, and finally the design of the intelligent educational system was carried out. In the two datasets selected in this paper, the knowledge graph-based learning content recommendation model has the highest F1 value of 98.67% and 97.43%, respectively, and the Q&A system designed in this paper got 75.6% correct among the selected 120 Q&As. It shows that the intelligent education system designed in this paper realizes good interaction effect and course content recommendation update effect.

Authors

Institutions

Publication Details

Journal
Applied Mathematics and Nonlinear Sciences
Published
2025-01-01
DOI
https://doi.org/10.2478/amns-2025-1075
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Curriculum Content Construction and Updating for Intelligent Educational Systems Using Knowledge Graphs

Lujie Fan
Applied Mathematics and Nonlinear Sciences
Advanced Graph Neural Networks
article

Curriculum Content Construction and Updating for Intelligent Educational Systems Using Knowledge Graphs

Lujie Fan
article en

Abstract

Abstract In an intelligent education system, accurately updating and constructing course content helps to improve the intelligence of the system and the learning efficiency of students, so this paper constructs an intelligent education system using knowledge graph. The system accomplishes ontology construction, extraction, storage and intelligent question and answer of knowledge through knowledge graph technology to realize the integration of course content. The BI-LSTM-CRF model was used for entity naming of the system’s Q&A function, the Q&A was categorized on the basis of feature extraction, and finally the design of the intelligent educational system was carried out. In the two datasets selected in this paper, the knowledge graph-based learning content recommendation model has the highest F1 value of 98.67% and 97.43%, respectively, and the Q&A system designed in this paper got 75.6% correct among the selected 120 Q&As. It shows that the intelligent education system designed in this paper realizes good interaction effect and course content recommendation update effect.

Applied Mathematics and Nonlinear SciencesVol. 10(1)
Jiaxing Vocational Technical College (CN), Henan Institute of Technology (CN), Xinxiang University (CN)
Openalex Percentile: Top 86%
Advanced Graph Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.