基于知识图谱的伤寒论课程学习资源推荐模型
Learning Resource Recommendation Model of Treatise on Febrile Diseases Course Based on Knowledge Graph
楚龙翔 1陶涛 1陈国龙 1易宇翔 1吴豪威 1刘东波1
作者信息
- 1. 湖南中医药大学 信息科学与工程学院,湖南 长沙 410208
- 折叠
摘要
结合知识图谱和个性化资源推荐算法,实现伤寒论课程学习资源的个性化推荐,帮助学习者更有效地学习.利用前期研究的知识图谱,融合课程本体知识,构建伤寒论课程知识图谱.在此基础上,结合协同过滤和基于路径的推荐算法,设计混合推荐算法,按相似度排序推荐学习资源给用户.提供形式化的知识表示方法和组织模型,满足个性化学习需求,为学习者提供符合其学习路线的资源,实现精准推送.该模型能够有效地解决伤寒论课程教学中存在的"晦涩难读懂、枯燥难专注、教学难互动"的问题,有助于学习者更好地学习伤寒论课程.
Abstract
Combined with the knowledge graph and personalized resource recommendation algorithm,the personalized recommendation of the course learning resources of Treatise on Febrile Diseases is realized to help learners learn more effectively.The knowledge graph in the early research is used to integrate the knowledge of the course ontology to construct the course knowledge graph of Treatise on Febrile Diseases.On this basis,combined with collaborative filtering and path-based recommendation algorithms,a hybrid recommendation algorithm is designed to recommend learning resources to users according to similarity ranking.It provides formal knowledge representation methods and organizational models to meet personalized learning needs,provides learners with resources that are in line with their learning paths,and achieves accurate push.This model can effectively solve the problems of"obscure and difficult to read,boring and difficult to focus,and difficult to interact in teaching"in the course teaching of Treatise on Febrile Diseases,which is helpful for learners to better learn the course of Treatise on Febrile Diseases.
关键词
课程知识图谱/伤寒论/资源推荐算法/个性化学习Key words
course knowledge graph/Treatise on Febrile Diseases/resource recommendation algorithm/personalized learning引用本文复制引用
基金项目
湖南省大学生创新训练项目(S202210241042)
湖南省中医药管理局科研课题(C2023018)
出版年
2024
国家科技期刊平台
NSTL
万方数据