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

这项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。信息发现从寻找与问题相关的信息资源开始；它通过识别资源所代表的想法之间的联系来继续，并综合新的想法。虽然目前的搜索引擎帮助人们查找相关信息，但它们以固定的文本列表形式呈现结果，这些文本列表是它们所代表的信息的弱认知替代品。将图像和文本结合在一起的表示可以更好地刺激理解、导航、学习和发现。此外，对表征的操纵有助于人们看到元素之间的关系，从而使认知重构成为发现的关键阶段。构图是将元素以新的结构组合在一起的过程，具有有意的关系，形成一个整体。为了促进信息发现，新的工具必须将搜索和浏览与收集和合成联系起来，从而流畅地集成信息发现、理解和重组的过程。该项目将开发和研究一个使用综合图文代理的混合主动系统，旨在促进在学习中发现信息。在混合主动系统中，代理与用户并发并主动地工作以完成任务。这个实验系统将形成图像-文本代理，使用语义模型来表示文档，并选择用户最感兴趣的文档。该系统将被部署在一门由理工科和商科学生开设的关于设计和发明的课程(“设计过程”)中，评估其对发现和学习的影响。这项跨学科的研究结合了教育学、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。预期成果包括：(1)将信息检索和模式识别方法与新颖的高级视觉界面相结合；(2)采用图文一体化的新的自动化方法表示文件；(3)对发明家和其他设计创新者的做法和需求有新的见解；(4)将创造性融入教育的新方法。先进的学习技术将通过组合信息软件平台实施和分发，用于混合主动的浏览、搜索、收集和写作，每年将有1000名学生在设计过程课程的作业中使用该平台，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