Promoting Information Discovery in Learning: Mixed-Initiative Composition of Integrated Image-Text Surrogates

本研究开发了新的工具，重新定义了内容和技术的角色，以促进学习中的发现。信息发现始于寻找与问题相关的信息资源；它通过识别资源所代表的思想之间的联系，并综合新的思想来继续。虽然当前的搜索引擎帮助人们找到相关信息，但它们将结果呈现为固定的文本列表，这是它们所代表的信息的弱认知替代品。结合图像和文本的表示可以更好地促进理解、导航、学习和发现。此外，表征的操作帮助人们看到元素之间的关系，从而实现认知重构，这是发现的重要阶段。构图是将元素以新的结构组合在一起，形成一个整体的过程。为了促进信息发现，新的工具必须将搜索和浏览与收集和组合连接起来，从而将信息发现、理解和重组的过程流畅地集成在一起。本项目将开发和研究一个混合倡议系统，使用集成的图像-文本替代，旨在促进学习中的信息发现。在混合主动系统中，智能体同时主动地与用户一起完成任务。这个实验系统将使用语义模型形成图像-文本替代品来表示文档，并选择用户最感兴趣的那些。该系统将被部署在科学、工程和商业学生的设计和发明课程（“设计过程”）中，在那里它对发现和学习的影响将被评估。这项跨学科研究将教育、图书馆学、认知心理学（包括创造性认知）、人机交互、数字图书馆、视觉艺术与设计、信息检索和模式识别等方法结合起来。预期结果包括：(1)信息检索和模式识别方法与新颖先进的视觉界面的集成；(2)结合图像-文本代理的自动表示文档的新方法；(3)对发明者和其他外观设计创新者的实践和需求的新见解；(4)创新融入教育的新方法。先进的学习技术将通过combinFormation软件平台实施和分发，用于混合主动浏览，搜索，收集和组合，每年将有1000名学生在设计过程课程中使用该软件，并通过互联网免费向公众提供

This research develops new tools that redefine the roles of content and technology to promote discovery in learning. Information discovery begins with finding information resources that are relevant to a problem; it continues through recognizing connections between ideas that the resources represent, and synthesizing new ideas. While current search engines help people find relevant information, they present results as fixed lists of text, which are weak cognitive surrogates for the information that they represent. Representations that combine images and text function as better stimuli for understanding, navigation, learning, and discovery. Further, manipulation of representations helps people to see relationships between elements, and thus enables the cognitive restructuring that is an essential stage of discovery. Composition is the process of putting elements together in new structures, with intentional relationships, to form a whole. To promote information discovery, new tools must connect searching and browsing with collecting and composing, and thus fluidly integrate processes of information finding, understanding, and restructuring. This project will develop and study a mixed-initiative system using integrated image-text surrogates, aimed at promoting information discovery in learning. In mixed-initiative systems, agents work concurrently and proactively with the user to accomplish a task. This experimental system will form image-text surrogates to represent documents using a semantic model, and select those that are most interesting to the user. The system will be deployed in a course on design and invention ("The Design Process") taken by science, engineering, and business students, where its impact on discovery and learning will be assessed. This interdisciplinary research connects methods from education, library science, cognitive psychology (including creative cognition), human computer interaction, digital libraries, visual art and design, information retrieval, and pattern recognition. Expected outcomes include (1) integration of information retrieval and pattern recognition methods with novel advanced visual interfaces; (2) new automatic methods for representing documents with integrated image-text surrogates; (3) new insights about the practices and needs of inventors and other design innovators; and (4) new methods for integrating creativity into education. The advanced leaning technologies will be implemented and distributed through the combinFormation software platform for mixed-initiative browsing, searching, collection, and composition, which will be used annually on assignments by 1000 students in The Design Process course, and made freely available to the public through the internet