摘要
传统教育知识图谱构建主要围绕学科与课程展开,而在智慧教育学习平台应用中,通常忽略了学习路径与学生学习特点对学习资源推荐的影响。为解决这一问题,文章探索了构建融合学习者、学习资源以及学习交互行为的智慧教育知识图谱,首先构建了智慧教育本体模型,然后将本地区智慧教育学习平台的数据作为源数据,进行知识抽取与知识融合,以形成智慧教育知识图谱,最后基于该模型在精准教学与资源推荐等领域的应用进行了深入探讨。
The construction of traditional educational knowledge graphs mainly revolves around disciplines and courses,while in the application of smart education learning platforms,the influence of learning paths and student learning characteristics on learning resource recommendations is usually ignored.To address this issue,the article explores the construction of a smart education knowledge graph that integrates learners,learning resources,and learning interaction behavior.Firstly,a smart education ontology model was constructed,and then the data from the local smart education learning platform was used as source data for knowledge extraction and fusion,forming a smart education knowledge graph.Finally,the application of this model in precision teaching and resource recommendation fields was deeply explored.
作者
胡志敏
HU Zhimin(College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China;Dagao Experimental School,Binzhou,Shandong 256802,China)
出处
《计算机应用文摘》
2024年第6期22-25,共4页
Chinese Journal of Computer Application
基金
滨州市教育科学研究院智慧教育专项课题项目:基于知识图谱的初中数学个性化学习资源推送研究(BZZH-001)。
关键词
智慧教育
本体
知识图谱
smart education
ontology
knowledge graph
