个性化网络教学论文数据挖掘技术在网络教学系统中的应用.doc

个性化网络教学论文：数据挖掘技术在网络教学系统中的应用 【中文摘要】随着网络对各个领域的渗透,基于Web的网络教学正在日益发展,它作为传统的集中式教学方式的一种补充有着其不可替代的优势。学生的学习方式发生了改变,不拘泥于地点和时间的限制,随时可以进行网上学习。但目前的网络教学存在其不足之处,不能充分考虑学生的各自特点,制定有个性化的教学内容和教学策略,于是本文基于数据挖掘技术和学生模型理论对网络教学系统进行了研究。使用了关联规则算法和聚类算法,关联规则挖掘就是从大量的数据中挖掘出有价值的、描述数据项之间相互联系的有关知识。聚类挖掘是将一群物理的或抽象的对象,根据它们之间的相似程度,分为若干组,其中相似的对象构成一组。本文首先分析了国内外个性化网络教学的发展现状与存在问题,进而得出进行个性化网络教学的必要性,只有进行个性化教学,才能更好地学生服务,提高学生的学习效率,激发学生的学习兴趣,以达到更好的教学效果;接着分别介绍了网络教学与数据挖掘技术的相关知识;然后提出了网络教学中学生模型的设计,并对学生模型进行细化成学生的知识模型、认知模型、媒体偏好模型、学习情绪模型、社会特征模型、以及情感意动模型这六个子模型;最后在个性化网络教学系统学生模型中,采用聚类技术和关联规则算法来对学生的学习特征进行分析挖掘,按照学生层次、学习能力、学习偏好和兴趣等对学生进行分类,发现一定的有用的学习模式,并据此确定不同类型学生的知识需求,教师根据系统提供信息调整相应的教学内容和教学策略,为学生提供更适合的学习方法,从而达到提高个性化教学系统智能性、更好地为各种学生群体提供有针对性的服务、真正达到因材施教的。 【英文摘要】With the network penetrating into various fields, a Web-based network education is also flourishing more and more. As a certain supplement of the traditional central education, the network-based education has the superiority which cannot be substituted. People can study without the limitation of the time and location. But the present network-based educational system still has its deficiencies, it can抰 consider students抍haracteristics adequately to set the personalized teaching contents and teaching strategies. According to above insufficiencies, based on data mining technology and student models theory, the thesis has studied an intelligent network-based educational system. Association rules and clustering are used in the thesis. Association rules mining can mine valuable and data relationship knowledge from a lot of data. Clustering mining can divide a group of physical or abstract objects into several groups according to the similitude degree between them. Among them, the similar object constitutes a group.First, the thesis analyzes the development status and problems of domestic and foreign individualized network learning. Then the necessity of individualized network learning is obtained. Only undertake personalized teaching, it can better student services, improv