Design of Computer-Aided Instruction Model Based on Knowledge Graph Construction and Learning Path Recommendation

This paper proposes a computer-aided teaching model using knowledge graph construction and learning path recommendation. It first creates a multimodal knowledge graph to illustrate complex relationships among knowledge. Learning elements and sequences are then used to form time sequences stored as directed graphs, supporting flexible path recommendations. Learners select elements based on interests and learning bases, updating behavior data for precise path recommendations. The platform, employing distributed architecture, integrates data processing and teaching applications for comprehensive cycle management and assessment. Controlled experiments validate its efficacy in enhancing learning outcomes compared to traditional methods, catering to personalized learning needs and advancing intelligent teaching.

Authors

Publication Details

Journal
International Journal of Web-Based Learning and Teaching Technologies
Published
2025-01-09
DOI
https://doi.org/10.4018/ijwltt.366803
Citations
2
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
4.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of Computer-Aided Instruction Model Based on Knowledge Graph Construction and Learning Path Recommendation

Fan Yang, Bin Meng
2 citations
International Journal of Web-Based Learning and Teaching Technologies
Intelligent Tutoring Systems and Adaptive Learning
4.96
article

Design of Computer-Aided Instruction Model Based on Knowledge Graph Construction and Learning Path Recommendation

Fan Yang, Bin Meng
article en
2 citations

Abstract

This paper proposes a computer-aided teaching model using knowledge graph construction and learning path recommendation. It first creates a multimodal knowledge graph to illustrate complex relationships among knowledge. Learning elements and sequences are then used to form time sequences stored as directed graphs, supporting flexible path recommendations. Learners select elements based on interests and learning bases, updating behavior data for precise path recommendations. The platform, employing distributed architecture, integrates data processing and teaching applications for comprehensive cycle management and assessment. Controlled experiments validate its efficacy in enhancing learning outcomes compared to traditional methods, catering to personalized learning needs and advancing intelligent teaching.

International Journal of Web-Based Learning and Teaching TechnologiesVol. 20(1)
Quality Education
Openalex Percentile: Top 6%
Intelligent Tutoring Systems and Adaptive Learning
4.96
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.

Design of Computer-Aided Instruction Model Based on Knowledge Graph Construction and Learning Path Recommendation — Fan Yang, Bin Meng · International Journal of Web-Based Learning and Teaching Technologies (2025) | TGRS Research Map | TGRS