Knowledge Discovery and modeling in Intelligent Tutoring Systems

知识获取一直是知识系统的主要瓶颈。这对于知识库包括领域本体和问题解决知识的智能教学系统（ITS）来说尤其如此。 为了使信息技术系统在知识发展迅速的领域，如商业和工业环境中蓬勃发展，必须开发新的、易于使用的工具，以获取和维护知识。第一个知识创建问题的根源在于知识库通常必须从头开始手动创建。然而，关键信息已经存在，隐藏在社区和组织内大量现有的文件和数据库中。领域专家需要使其明确，并提供相关的知识，人工导师，使它能够通过能力和解决问题的技能发展指导学习者。然而，在几个领域，解决问题的知识是不容易预先确定的，它evolved.To找到更有效的解决这些问题，我建议调查知识发现（KD）技术，以建立领域内的智能ITS。 首先，该项目将产生一些KD技术，可以支持新的领域知识发现和本体进化的动态域中的挖掘文档流的上下文中学习将发生。第二，我感兴趣的是从用户的动作轨迹中自动学习任务模型，以提取新的程序（正确或不正确），新的问题空间和新的问题解决策略。我们知道认知任务分析和规则编写过程是多么耗时，这些过程旨在产生有效的问题空间或任务模型，以支持有价值的辅导服务，如模型和知识跟踪、辅导、错误检测和计划识别。当前的创作系统未能简化这些过程。这个项目提供了一个很好的机会，以克服这两个主要的绊脚石知识获取的ITS。

Knowledge acquisition has always been the major bottleneck for knowledge-based systems (KBS). This is especially true for Intelligent Tutoring Systems (ITS) in which knowledge base includes domain ontology and problem solving knowledge.  In order for ITSs to thrive in fields where knowledge evolves rapidly, such as business and industrial settings, new, easy-to-use tools must be developed for knowledge acquisition and maintenance. The first knowledge creation problem finds its source in the fact that the knowledge base usually has to be created manually, from scratch. However, crucial information already exists, hidden in legions of existing documents and databases within communities and organizations. Domain expert are required to make it explicit and provide relevant knowledge to the artificial tutor so that it will be able to guide the learner through competence and problem solving skills development. However, in several domains, problem solving knowledge is not easy to predetermine and it evolves.To find more efficient solutions to these problems, I propose to investigate Knowledge Discovery (KD) techniques to build domain intelligence within an ITS.  First, this project will produce some KD techniques that can support new domain knowledge discovery and ontology evolution in dynamic domains by mining documents flowing in the context where learning will take place. Second, I am interested in the automatic learning of task models from users' action traces to extract new procedures (correct or incorrect), new problem spaces and new problem solving strategies. We know how time-consuming are both the cognitive task analysis and rules authoring process aimed at producing effective problem spaces or task models to support valuable tutoring services such as model and knowledge tracing, coaching, error detection and plan recognition. Current authoring systems fail to simplify these processes. This project offers a great opportunity to overcome those two main stumbling blocks of knowledge acquisition in ITS.