ITR-(ASE+ECS)-(soc+sim+int)-Natural Language Processing Technology for Guided Study of Bioinformatics

从教育的角度来看，自然语言处理的最新进展，特别是使用非结构化数据回答自然语言问题的能力，是非常令人兴奋的。它们提供了自动回答学生问题的系统的承诺，从而不仅支持引导的学习方法，而且支持开放的、基于探索的学习方法。开发支持学生学习的软件完全是为了为学生构建一种合适的环境，一个促进而不是抑制通过已知知识空间进行探究的环境，并为试图发现或生成新知识提供一个起点空间。该项目的目标是应用计算机科学--特别是自然语言处理--和学习科学的研究，开发一种能够提供这种所需环境的智能导师。这位教师将生活在一个人机交互环境中，在这个环境中，计算机能够检测和跟踪用户的认知和学习状态，并根据这些知识采取行动，帮助学生识别和获取相关知识，提供学生可能需要的相关事实信息，并指导学生选择潜在的相关子任务。这个项目的试验台领域涉及学习生物信息学概念的高中生和本科生--建立在生物信息学社区免费提供的大量生物数据和软件的基础上，特别是利用NCSA开发的生物学工作台系统。在这个项目的背景下，研究人员将(1)开发必要的机器学习、自然语言和推理方法，这些方法能够有力地支持自然语言理解的水平，足以“理解”学生和他们的问题，足以将他们引向正确的材料，提出相关建议，并在主题的背景下开展有意义的对话；(2)建立一个能够适应不同学生背景和目标的系统，并适当地进行学习；及(3)研究学生如何学习，以及如何在计算机辅助环境中支持学生的学习。这项计划将有助于理解学生如何在计算机辅助环境中学习，并利用它来开发在这些环境中支持学习的改进方法。这有可能对大班、远程教育和自定进度的教学产生巨大的教育影响。该项目在基于自然语言的人机交互、自适应对话管理、用户敏感信息检索和提取以及机器学习等领域的计算结果将广泛适用于许多其他领域，包括老年人和其他群体的智能信息获取和互动支持系统。

Recent advances in Natural Language Processing, in particular the ability to use unstructured data to answer natural language questions, are very exciting from an educational perspective. They offer the promise of systems that can automatically respond to students' questions, thus supporting not only a guided but also an open ended, exploration based, approach to learning.Developing software that supports students' learning is all about constructing the right kind of environment for students, one that facilitates rather than inhibits inquiry through a known knowledge space and provides a jumping-off space for trying to find or generate new knowledge.The goal of this project is to apply research in Computer Science -- particularly Natural Language Processing -- and the Learning Sciences, to developing an intelligent tutor that can provide this needed environment. This tutor will inhabit a human-computer interactive environment in which the computer is able to detect and track the user's cognitive and academic state and act based on this knowledge to aid the student in identifying and accessing relevant knowledge, contribute relevant factual information the student may need and guide the student in selecting potentially relevant subtasks.The testbed domain in this project involves high school and undergraduate level students studying concepts in Bioinformatics -- building on the enormous amounts of biological data and software made freely available on the Web by the Bioinformatics community, and specifically, making use of the Biology Workbench system developed at NCSA.In the context of this project, researchers will (1) develop the necessary machine learning, natural language and inference methods that can robustly support a level of natural language understanding that is sufficient to ``understand'' students and their queries well enough to direct it to the right material, make relevant suggestions and develop a meaningful dialog in the context of the subject matter; (2) create a system that is able to accommodate different student backgrounds and goals and behave appropriately, and learn as it does so; and (3) study how students learn and how to support students' learning in a computer-aided context.This project will contribute to the understanding of how students learn in a computer aided environment and use it to develop improved methods for supporting learning in these environments. This has the potential for large educational impact for large classes, distance education, and self-paced instruction. The project's computational results in areas such as natural language based human machine interaction, adaptive dialog management, user-sensitive information retrieval and extraction, and machine learning, would be widely applicable to many other domains, including intelligent information access and interactive support systems for senior citizens and other groups.