课程教学中基于模糊规则的知识网推荐方法

Knowledge mesh recommendation method based on fuzzy rules for course teaching-learning

针对现有推荐方法未能充分考虑学习者的学习目的以及学习者认知水平的动态不确定性问题,提出了一种基于模糊规则的知识网推荐方法.该方法将课程老师的教学经验和已学过该课程学习者的学习路径分别组成知识网,综合考虑做题时间、试题难度和记忆量等因素来确定学习者认知水平,融合匹配度和认知水平来计算知识网之间的相似度,采用模糊C均值聚类方法对知识网进行聚类.以15名学习者的学习路径为试验数据,构建知识网W_(1)-W_(15),计算出W_(1)-W_(15)的模糊化认知水平和隶属度,将隶属度最大的知识网优化组合成一个新的知识网.结果表明,文中方法可以为学习者推荐适合其学习目的的学习路径,合理反映学习者的认知过程.

To solve the problem that the current recommendation methods could not fully consider the learning objectives of learners and the dynamic uncertainty of learner′s cognitive level,a knowledge mesh recommendation method was proposed based on fuzzy rules.In the proposed method,the teaching experience of the course teacher and the learning path of the learners who had learned the course were respectively formed into each knowledge mesh.The cognitive level of the learners was determined by comprehensively considering the factors of question-making time,test difficulty and memory.The similarity between the knowledge meshes was calculated by fusing the matching degree and cognitive level,and the knowledge meshes were clustered by the fuzzy C-mean clustering method.Taking the learning paths of 15 learners as experimental data,the knowledge meshes of W_(1)-W_(15)were constructed,and the fuzzy cognitive level and membership degree of W_(1)-W_(15)were calculated.The knowledge mesh with the largest membership degree was optimized and combined into a new knowledge network.The experimental results show that the proposed recommendation method can recommend suitable learning paths for learners′learning purpose and reasonably reflect learners′cognitive process.