构建教育教学与智能技术深度融合的知识图谱成为智慧教育领域发展的新思路.针对单片机课程概念繁多、抽象难懂,学习过程知识碎片化严重等问题,提出一种单片机知识图谱的构建方法.采用七步法完成本体构建,分别利用Bi-LSTM-CRF(Bidirectional Long Short Term Memory Conditional Random Fields)和BERT(Bidirectional Encoder Representations from Transformer)模型进行实体抽取与关系抽取,实现知识存储与可视化.课程知识图谱增强了学习的直观性,为个性化教学、学习路径推荐和评价反馈提供了基础,有助于提升教学质量,推动单片机课程的创新发展.
Knowledge Graph Construction of Microcontroller Courses for Intelligent Education
Constructing a knowledge graph for the deep integration of education and teaching with intelligent technology has become a new idea for the development of the field of intelligent education.Addressing the challenges inherent in the complex concepts and abstract content within Microcontroller courses,as well as the substantial fragmentation of knowledge within the learning process,this study proposes a method for constructing a Microcontroller course-specific knowledge graph.The construction of this ontology adheres to a seven-step methodology,subsequently employing Bidirectional Long Short Term Memory Conditional Random Fields(Bi-LSTM-CRF)and Bidirectional Encoder Representations from Transformer(BERT)models for the extraction of entities and their interrelationships,enabling efficient knowledge storage and visualization.The course-oriented knowledge graph ameliorates the intuitiveness of the learning experience,laying a robust groundwork for personalized pedagogical approaches,prescriptive learning path recommendations,and comprehensive evaluation feedback mechanisms.This contributes substantially to the amelioration of instructional quality,catalyzing innovative advancements in Microcontroller curriculum development.
万方数据
维普
