Collaborative Research: Enabling Robust Learning with Conceptual Personalization Technologies

个性化指导？针对个别学生的指导？独特的学习需求，并建立在他们以前的知识？对支持有效的科学学习至关重要。 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领域的技术，以创建个性化算法，而学习科学目标是测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。这包括自动识别主题处理中的核心概念。其次，在学习过程中，系统将分析学生的论文，以动态构建学生回答的领域知识地图（以及对学生理解的评估）。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，该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。这个项目，通过自动化的许多过程，以确定知识和关键概念，有可能改变学习。该系统独立于学习领域，因此可以用于任何科学领域。这个系统也不依赖于熟练的教师吗？因此，它可以有效地用于服务不足的学校。

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, and 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 to automatically process learning resources (primarily texts) in order to create a domain knowledge map. This includes automatically identifying core concepts in a treatment of the subject matter. 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.This project, by automating many of the processes to identify knowledge and key concepts, has the potential to transform learning. The system is independent of the domain of learning so it can be used for any area of science. The system also does not depend upon skilled teachers ? so it can be effectively used in under-served schools.