知识图谱与教育大数据协同驱动的自适应学习模式研究

Research on Self-adaptive Learning Mode Driven by the Collaboration of Knowledge Graph and Educational Big Data

为了有机融入领域知识的内在信息与关联,将知识图谱引入智慧教学过程,构建了知识图谱与教育大数据协同驱动的自适应学习模式。通过对多源、异构的大数据进行数据采集与数据分析,认知学生的学习行为特征,为自适应学习构筑重要前提;将课程的知识体系化,构建多维度下的知识图谱,其拓扑结构蕴含领域专家经验、知识关联与学习路径,为基于课程内容的自适应学习奠定基础;将知识图谱与教育大数据进行深度融合,为学生的自适应学习提供量化的、实时的决策支持。新模式实现了规模化教育与个性化培养的有机结合,提供了具有泛化能力的新型智慧教学思路,有利于开展自适应学习资源推送、自适应学习路径规划与自适应学习预警等智慧教学应用与实践。

In order to integrate the internal information and association of domain knowledge organically,the knowledge graph is introduced into the smart teaching process,and an self-adaptive learning mode driven by the collaboration of knowledge graph and education big data is constructed.Through data collection and data analysis of multi-source,heterogeneous big data,recognize the learning behavior characteristics of students,and build an important prerequisite for adaptive learning.By systematizing the knowledge of courses,a multidimensional knowledge graph is constructed,whose topology contains domain expert experience,knowledge association and learning path,laying a foundation for self-adaptive learning based on course content.The knowledge graph is deeply integrated with educational big data to provide quantitative and real-time decision support for students’self-adaptive learning.The new model realizes the organic combination of large-scale education and individualized training,and provides a new intelligent teaching idea with generalization ability,which is conducive to the development of self-adaptation,smart teaching applications and practices such as learning resource push,self-adaptive learning path planning,and self-adaptive learning early warning.