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

Autor/inn/en	Liu, Chunhong; Zhang, Haoyang; Zhang, Jieyu; Zhang, Zhengling; Yuan, Peiyan
Titel	Design of a Learning Path Recommendation System Based on a Knowledge Graph
Quelle	In: International Journal of Information and Communication Technology Education, 19 (2023) 1, (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Yuan, Peiyan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1550-1876
Schlagwörter	Educational Technology; Artificial Intelligence; Electronic Learning; Concept Mapping; Graphs; Network Analysis; Algorithms; Learning Processes; Design; Feedback (Response) + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Künstliche Intelligenz; Concept Map; Grafische Darstellung; Netzplantechnik; Algorithm; Algorithmus; Learning process; Lernprozess
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01