一种网络导航学习路径生成方法

该文提出了一种基于反馈的、满足全局优化和充分必要条件的网络导航学习路径生成方法。将知识单元及其依赖关系作为知识地图的顶点和边,全部存储于广义表中。测试学习者已经学过的知识单元,根据知识单元中心度和难度量化计算方法,生成知识地图总路径集合,基于不同的学习基础,学习者在集合中“按需所取”知识单元。该方法产生的知识地图导航不再是海量知识单元的不完备近似集合,而具有全局精确性;解决了学习路径的充分性和必要性难以同时满足的缺陷;针对学习者个性化要求,有效提高学习效率,具有教育学和计算机科学的双重价值。

Network Navigation Learning Method Based on Feedback Mechanism

A novel method on network learning is proposed. It can optimize the knowledge map in the global conditions and the necessary and sufficient requirements. All the knowledge units and their relations, stored in a generalized list, are viewed as the vertices and edges in the knowledge map. After testing the knowledge units which have been learned by users before, we can get the knowledge levels corresponding with each user. The sort sets of knowledge units can be deduced according to the algorithms of knowledge unit central and degree of difficulty, and then the complete sets of knowledge map paths are achieved. The approximation is substituted by the precision in the proposed method, and the defect that the necessary and sufficient conditions cannot be satisfied simultaneously is solved as well. The network navigation paths in our knowledge map can be used to avoid the problems of disorientation and information overloading, which can improve the efficiency of user learning on the network.