Collaborative Research: Enabling Robust Learning with Conceptual Personalization Technologies

个性化的教学指导，针对个别学生的独特的学习需求，并建立在他们的先验知识是支持有效的科学学习的关键。该项目的主要目标是使用自然语言技术通过个性化策略支持健壮的学习。该项目是科罗拉多大学、大学大气研究公司和犹他州大学之间的三个机构合作项目。它有两个目标：技术目标是创建独立于域的技术来创建个性化算法。学习科学的目标是衡量这些算法对学习的影响。该项目的重点是强大的学习，即，学习支持迁移和元认知技能的提升。主题是地球科学和生物学。拟议的技术将按以下方式运作。首先，该系统使用最先进的统计自然语言处理方法（主要是潜在语义分析）来自动处理学习资源（主要是文本），以创建领域知识地图。这包括自动识别核心概念。其次，在学习过程中，系统将分析学生的文章，以动态构建学生回答的领域知识地图（以及学生理解的评估）。使用图形匹配技术，该系统评估学生的反应，包括确定哪些概念被遗漏或误解。最后，系统使用推荐引擎的方法来建议网络资源，可以帮助学生理解的材料。在学习评估方面，该项目采用2（技术或无）乘2（学习领域）的混合研究设计，样本为40名学生。该项目的目的是使科学学习和教学更有效。该项目利用了一套丰富的新工具来分析文本材料。这些工具有几个功能。它们最初允许文本课程材料的分析，以自动开发嵌入在文本中的核心概念的描述。然后，这些工具评估学生撰写的有关核心材料的论文，以确定他们个人的理解水平。然后，这些工具向个别学生提供关于遗漏或误解概念的反馈。这种反馈包括参考包括学习过程中的文本材料和参考其他互联网资源，个别学生可以使用这些资源来更好地理解生物学和生物科学中的科学概念。这项研究建立在15年的文本语义分析工作的基础上。这项研究意义重大，因为它适用于任何学科领域，而且由于它是自动化的，它可以在全国范围内扩展。

Personalized instruction instruction that targets individual students' unique learning needs and builds upon their prior knowledge is critical for supporting effective science learning. The primary goal in this project is to support robust learning with personalization strategies using natural language technologies. The project is a three-institution collaboration between the University of Colorado, the University Corporation for Atmospheric Research, and the University of Utah. It has two objectives: the technology objective is to create domain-independent techniques to create personalization algorithms. The learning science objective is to measure the effect of these algorithms on learning. The project focuses on robust learning, i.e., learning the supports transfer and the promotion of meta-cognitive skills. The subject matter is earth science and biology. The proposed techonology would operate as follows. Firstly, the system uses state-of-the-art statistical natural language processing methods (mainly latent semantic analysis) to automatically process learning resources (primarily texts) in order to create a domain knowledge map. This includes automatically identifying core concepts. Secondly, during learning sessions, the system would analyze students' essays to dynamically construct a domain knowledge map of the students' responses (and an assessment of student understanding). Using graph matching techniques, the system evaluates the student's response, including determining what concepts were missing or misunderstood. Finally, the system uses recommendation engine methods to suggest web resources that could help the student understand the material. For learning assessment, the project uses a 2 (technology or none) by 2 (domain of study) mixed research design using a sample of 40 students.The intent of this project is to make science learning and teaching more effective. The project draws upon a rich set of new tools for analyzing textual materials. The tools serve several functions. They initially allow the analysis of text course materials to automatically develop a description of the core concepts embedded in the texts. The tools then assess essays that the students write about the core materials to determine their individual level of understanding. The tools then provide feedback to the individual students regarding missing or misunderstood concepts. This feedback includes references to the text materials that comprise the course of study and references to additional internet resources that the individual students can use to better understand scientific concepts in biology and earch sciences. This research builds upon 15 years of work on semantic analysis of texts. The research is significant because it works for any subject area and, since it is automated, it can scale nationwide.