Design of a Learning Path Recommendation System Based on a Knowledge Graph

Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the char...

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Vydáno v:International journal of information and communication technology education Ročník 19; číslo 1; s. 1 - 18
Hlavní autoři: Liu, Chunhong, Zhang, Haoyang, Zhang, Jieyu, Zhang, Zhengling, Yuan, Peiyan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hershey IGI Global 01.01.2023
Témata:
Algorithms
Artificial Intelligence
CAI
Computer assisted instruction
Concept Mapping
Design
Educational activities
Educational Resources
Educational Technology
Electronic Learning
Feedback (Response)
Function words
Grammatical aspect
Graphs
Intelligence
Knowledge
Knowledge representation
Learning
Learning Processes
Learning resources
Mathematics
Network Analysis
Recommender systems
Simulated annealing
Social networks
Students
Swarm intelligence
Teachers
Teaching
ISSN:1550-1876, 1550-1337
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Shrnutí:Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally represent subject knowledge. The system uses the node centrality and node weight to expand the knowledge graph system, which can better express the structural relationship among knowledge. It applies the particle swarm fusion algorithm of multiple rounds of iterative simulated annealing to achieve the recommendation of learning paths. Furthermore, the system feeds back the students' learning situation to the teachers. Teachers check and fill in the gaps according to the performance of the learners in the teaching activities. Aiming at the weak links of students' knowledge points, the particle swarm intelligence algorithm is used to recommend learning paths and learning resources to fill in the gaps in a targeted manner.
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ISSN:1550-1876
1550-1337
DOI:10.4018/IJICTE.319962