Algorithms and Applications of Learned Student Models for Adaptive Learning Systems

自适应学习系统的学习学生模型的算法和应用

• 批准号: RGPIN-2017-06776
• 负责人: Desmarais, Michel
• 金额: $ 1.46万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=649109
• 关键词: Algorithms; Applications; Learned; Student; Models

In the field of Intelligent Tutoring Systems, a critical modeling issue is to determine how the domain content relates to skills that we aim the student to learn. When this content represents tasks that can be succeeded or failed (question, exercise, etc.), the mapping of tasks to skills is the key to deliver a student skills assessment. When the content represents learning material, a tutoring system can use the mapping of skills to learning material in order to guide the student through the appropriate topics and reach specific learning goals.******A first objective of this research program is to design algorithms that can help map task to the required skills. We have made great progress in developing algorithms that can infer this mapping from data, and that refine and validate expert given mappings, but there is still room from improvement and we propose a number of research avenues in that respect.******A second objective of the program is to work on the mapping of learning content to skills. While the algorithms that map tasks to skill can use student performance data, such as test outcome, to infer the skills behind tasks, this type of data is not readily available for learning material. Material such as Wikipedia pages, textbooks chapters, or documents in general, is most often indexed by topics, not by the skills they can help acquire. Yet, learning objectives is best defined by skills, and the alignment of skills to concepts is not always clear, or even the one we would expect.******Therefore, standard techniques based on semantic distance between content and topics is deemed insufficient to determine the specific skills addressed by a given content. They cannot readily tell if the content is too difficult or already known by a student with a specific skill profile. Nor can they determined prerequesite skills and concepts that are involved in the content.******We plan to develop data driven approaches to determine the mapping of learning content to skills. These approaches will rely on textual features extraction techniques determine the related skills, and also make use of the techniques developed for tasks to skills mapping.

在智能辅导系统领域，一个关键的建模问题是确定领域内容如何与我们目标学生学习的技能相关。 当此内容表示可以成功或失败的任务（问题、练习等）时，任务与技能的映射是提供学生技能评估的关键。 当内容表示学习材料时，辅导系统可以使用技能到学习材料的映射，以便引导学生通过适当的主题并达到特定的学习目标。该研究计划的第一个目标是设计算法，可以帮助映射任务所需的技能。 我们已经在开发算法方面取得了很大的进展，这些算法可以从数据中推断出这种映射，并改进和验证专家给定的映射，但仍有改进的空间，我们在这方面提出了一些研究途径。该计划的第二个目标是将学习内容映射到技能。 虽然将任务映射到技能的算法可以使用学生的表现数据（例如测试结果）来推断任务背后的技能，但这种类型的数据并不容易用于学习材料。维基百科页面、教科书章节或一般文档等材料通常按主题索引，而不是按它们可以帮助获得的技能索引。 然而，学习目标最好由技能来定义，而技能与概念的一致性并不总是明确的，甚至不是我们所期望的。因此，基于内容和主题之间的语义距离的标准技术被认为不足以确定给定内容所涉及的特定技能。 他们无法轻易判断内容是否太难或已经被具有特定技能的学生所了解。 他们也不能确定内容中涉及的先决条件技能和概念。我们计划开发数据驱动的方法来确定学习内容与技能的映射。 这些方法将依赖于文本特征提取技术来确定相关技能，并利用为任务到技能映射开发的技术。