Domain Knowledge Modelling for Educational Purposes: Methods and Tools

拥有一个系统的想法，可以教一个给定的主题与最好的人类教师的有效性（即，智能辅导系统（ITS）自诞生以来一直处于人工智能许多应用的前沿。这些系统在许多领域具有巨大的潜力和重要的经济和社会效益。它们传达了人工智能的许多挑战性问题。其中一个问题是领域知识（即关于所教主题的知识）的获取和表示，以及对这些知识的推理。本研究建议的主要目标是开发方法和工具，以加强ITS与自动化设施能够建立，改进和维护一个有效的语义和程序领域知识模型。首先，我将开发新的方法来精炼、完善和改进ITS中的领域本体。这些方法将有助于信息技术系统的知识工程方面，以及其他以知识为基础的系统或知识管理系统。提供的工具，可以促进领域本体的创建和维护，将可能产生进一步的ITS与本体为基础的领域知识模块，从而更容易在不同的ITS之间共享领域知识。其次，我将详细阐述和测试有效的数据挖掘算法，可以产生有用的程序知识，从教育问题解决的数据，特别是在定义不清的领域。这些程序性知识将被用来为那些难以建立清晰任务模型的领域的学习者提供有用的辅导服务。我们希望我们的研究结果有助于目前的教育数据挖掘工作，提供有价值的替代品和互补的机会，ITS的发展在定义不清的领域。这项研究计划必将产生的方法和工具，这将特别提高ITS和电子学习软件在动态的，不断发展的领域的性能，不受传统的关注捕捉语义和程序领域知识。这些成果将为加拿大电子学习提供商提供软件和服务增强选项，并帮助其他加拿大公司改进其知识管理平台。

The idea of having a system that can teach a given subject with the effectiveness of the best human teacher (i.e., Intelligent Tutoring Systems - or ITS), has been at the fore front of many applications of artificial intelligence since its birth. These systems have tremendous potential and important economic and social benefits in many areas. They convey many challenging problems of AI. One such problem is the acquisition and representation of domain knowledge (that is, knowledge about the topic being taught), as well as reasoning about this knowledge. The main goal of this research proposal is to develop methods and tools to enhance ITSs with automated facilities capable of building, improving and maintaining an effective semantic and procedural domain knowledge model. First, I will develop new methods for refining, completing, and improving the domain ontology within an ITS. Those methods stand to contribute to the knowledge engineering aspects of ITSs, as well as other knowledge-based or knowledge management systems. The provision of tools that can facilitate domain ontology creation and maintenance, will likely produce further ITSs with ontology-based domain knowledge modules, and thereby make it easier to share domain knowledge between different ITSs. Second, I will elaborate and test effective data mining algorithms that can yield useful procedural knowledge from educational problem-solving data, particularly in ill-defined domains. Such procedural knowledge will be used to provide useful tutoring services to learners in domain where a clear task model is difficult to set up. We expect our findings to contribute to current educational data mining efforts, by offering valuable alternatives and complementary opportunities for the development of ITS in ill-defined domains. This research program is bound to produce methods and tools, that will particularly enhance the performance of ITS and e-learning software in dynamic, evolving domains, unburdened by traditional concerns for capturing semantic and procedural domain knowledge. Such results will offer Canadian e-learning providers software and service enhancement options, and help other Canadian companies to improve their knowledge management platforms.