Domain Knowledge Modelling for Educational Purposes: Methods and Tools

用于教育目的的领域知识建模：方法和工具

• 批准号: 217279-2012
• 负责人: Nkambou, Roger
• 金额: $ 1.6万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=523748
• 关键词: Domain; Knowledge; Modelling; Educational; Purposes

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.

自人工智能诞生以来，拥有一个能够以最好的人类教师的效率教授给定学科的系统(即智能教学系统--或ITS)的想法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具有挑战性的问题。一个这样的问题是领域知识(即关于正在教授的主题的知识)的获取和表示，以及对这些知识的推理。这项研究提案的主要目标是开发方法和工具，通过能够建立、改进和维护有效的语义和程序性领域知识模型的自动化设施来增强信息技术支持系统。首先，我将开发新的方法来提炼、完善和改进ITS中的领域本体。这些方法将有助于信息技术支持系统的知识工程方面以及其他以知识为基础的或知识管理系统。提供可促进领域本体创建和维护的工具，可能会产生更多具有基于本体的领域知识模块的信息技术支持系统，从而使不同的信息技术支持服务之间更容易共享领域知识。其次，我将详细阐述和测试有效的数据挖掘算法，这些算法可以从教育问题解决数据中产生有用的程序性知识，特别是在定义不明确的领域中。这些程序性知识将被用来为难以建立明确任务模型的领域的学习者提供有用的辅导服务。我们希望我们的发现为当前的教育数据挖掘工作做出贡献，为在定义模糊的领域中发展智能交通系统提供有价值的替代方案和补充机会。这一研究计划必然会产生方法和工具，特别是将提高智能交通系统和电子学习软件在动态、不断发展的领域的性能，而不受获取语义和程序性领域知识的传统问题的影响。这样的结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。