基于语义网络的知识建模和自动问题生成-《计算机技术与发展》

文章信息/Info

Title:: Semantic Network Based Modeling of Knowledge and Automatic Question Generation

Author(s):: ZHANG Kai; LIU Dong-feng; LIN Shun-qing; School of Information Engineering,Guangdong University of Technology,Guangzhou 510006,China

Keywords:: intelligent tutoring system; semantic network; knowledge representation; problem generation; knowledge base

摘要:: 目前大多数三维虚拟实验系统的研究都只是集中在实验操作的真实性与交互性上,缺少了对实验概念知识的学习过程,从而不能使学生很好地掌握与实验相关的知识。针对这个问题,开发了用于三维虚拟实验智能辅导系统的知识表示模块。引入的语义网络节点和语义关系不仅表示概念和概念之间的关系,还表示实验仪器图片信息和问题信息。通过对实验语义网络的解析,可快速推理出与实验相关的概念知识。另一方面,通过对实验知识的语义网络建模目的在于自动生成一系列与实验相关的学习问题,这些问题可以识别学生的答案并提供反馈。根据提出的方法,可为具体的实验建立一个语义网络知识库,知识库将作为概念知识和问题的来源,这样不仅减轻了系统的开发难度,也能够为学生提供个性化的学习环境。

Abstract:: At present, most of the researches on 3D virtual experimental systems only focus on the authenticity and interactivity of experimental operations, and lack the learning process of experimental knowledge. In response to this problem, a knowledge representation module for a 3D virtual experiment intelligent tutoring system was developed. The introduced semantic network nodes and semantic relations not only represent the relationship between concepts and concepts,but also represent experimental instrument picture information and problem information. Through the analysis of the experimental semantic network,conceptual knowledge related to the experiment can be quickly inferred. On the other hand,the purpose of modeling the semantic network of experimental knowledge is to automatically generate a series of experimental - related learning questions, which can identify students’ answers and provide feedback.According to the proposed method,a semantic network knowledge base can be established for specific experiments. The knowledge base will serve as the source of conceptual knowledge and problems. This not only reduces the difficulty of system development,but also provides students with a personalized learning environment.