基于扩展知识结构图的智能教学规划  

Intelligent Tutorial Planning Based on Extended Knowledge Structure Graph

作　　者：段琢华[1] 姜云飞[2] 

机构地区：[1]韶关学院计算机科学系,广东韶关512000 [2]中山大学软件研究所,广州510000

出　　处：《计算机工程与应用》2005年第17期193-196,共4页Computer Engineering and Applications

摘　　要：智能教学规划是智能教学系统的核心组成部分,领域知识模型、教学方法模型以及学习者模型是智能教学系统的三个要素。提出了扩展知识结构图(ExtendedKnowledgeStructureGraph,EKSG)的概念,通过扩展知识结构图将领域知识模型、教学方法模型、学习者模型有机地结合起来。在此基础上,给出了根据扩展知识结构图制定学习者关于学习目标的最优教学规划算法,规划算法由两部分组成:判定算法JUDGE和最优规划生成算法TPLAN,其中JUDGE判断规划问题是否有解,在有解的情况下求出最优解图,TPLAN根据最优解图计算最优规划。证明了算法的正确性,分析了算法的效率。Intelligent tutorial planning(ITP) is an important component of Intelligent Tutorial System(ITS).Models of domain knowledge,models of tutorial methods and models of learner are three key elements of ITS.In this paper,the concept of Extended Knowledge Structure Graph(EKSG) is presented.An EKSG integrates models of domain knowledge,models of tutorial methods and models of learner organically.Based on the EKSG,algorithms JUDGE and TPLAN are put forward to resolve ITP problems.The algorithm JUDGE calculates the optimal solution graph when there is a solution,while the algorithm TPLAN calculates optimal tutorial plan based on the solution graph.Both algorithms are proved to be correct,the efficiency of them is also discussed.

关 键 词：智能规划 智能教学系统 知识表示 远程教学 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]