Dissertation Research: Statistical Models of Hypertext Comprehension

论文研究：超文本理解的统计模型

• 批准号: 0624983
• 负责人: Richard Golden
• 金额: $ 1.06万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0624983&HistoricalAwards=false
• 关键词: Dissertation; Research; Statistical; Models; Hypertext

Hypertext has been attributed to reading comprehension difficulties. Yet under certain conditions, hypertexts with the appropriate semantic organization have been found to facilitate comprehension and recall. To address these issues, this study will involve hypertext readers from two groups: (i) undergraduate experts in Psychology who are Neuroscience novices, and (ii) undergraduate experts in Neuroscience who are Psychology novices. The readers will traverse three web sites associated with three different content domains for the purposes of generating summaries of the content domain on those websites. In addition, some web sites will present material to readers in a linear sequential format while other web sites will present material to readers in a hypertext format. Half of the hypertext formats will be "fully connected" (i.e., a reader will be able to traverse from any point in the web site to any other point), while the other half of the hypertext formats will be "semantically organized" (i.e., readers will be allowed to make their own traversal decisions but their decision choices will be restricted to traversing between semantically related web pages). Thus, improved navigation and memory performance for the semantic theory driven hypertext environment relative to the linear text environment and meshed-hypertext environment is expected for experts. Novices, unfamiliar with semantic structure of the expository hypertexts, should be most effective at navigating and recalling information presented within a linear text environment. This study will attempt to investigate how the previously defined factors of domain knowledge and content presentation influence web site traversal strategies and the organization of web site summaries. Classical data analysis will be used to identify these qualitative phenomena. In addition, Knowledge Digraph Contribution analysis (a theoretically-oriented statistically-robust categorical regression time-series analysis) of sequential structure in navigation patterns and student responses will be used to obtain a more detailed quantitative understanding of the data in terms of specific semantic network theories of knowledge organization. The long-term objective of this project is to support advancements in our scientific understanding of human comprehension in the hypertext environment. An improved understanding of hypertext comprehension will not only advance general understanding of human cognition but also will advance understanding of the conditions for designing web sites which are easier to understand. This project will provide cognitive psychologists, web designers, and Human-Computer Interaction specialists with new ways of observing effects of content presentation and domain knowledge upon navigation patterns and production data. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

超文本被归因于阅读理解困难。 然而，在一定条件下，超文本与适当的语义组织已被发现，以促进理解和回忆。 为了解决这些问题，本研究将涉及两组超文本读者：（i）心理学本科专家谁是神经科学新手，（ii）神经科学本科专家谁是心理学新手。 读者将遍历与三个不同内容域相关联的三个网站，以便在这些网站上生成内容域的摘要。 此外，有些网站将以线性顺序格式向读者提供材料，而另一些网站将以超文本格式向读者提供材料。 一半的超文本格式将是“完全连接的”（即，读者将能够从网站中的任何点遍历到任何其它点），而超文本格式的另一半将被“语义地组织”（即，读者将被允许做出他们自己的遍历决定，但是他们的决定选择将被限制为在语义相关的网页之间遍历）。 因此，改进的导航和记忆性能的语义理论驱动的超文本环境相对于线性文本环境和网状超文本环境的专家。 新手，不熟悉的语义结构的临时超文本，应该是最有效的导航和回忆信息中呈现的线性文本环境。 本研究将探讨先前定义的领域知识和内容呈现因素如何影响网站遍历策略和网站摘要的组织。 经典的数据分析将用于识别这些定性现象。 此外，知识有向图贡献分析（一种理论导向的分类回归时间序列分析）的序列结构的导航模式和学生的反应将被用来获得更详细的定量了解的数据在特定的语义网络理论的知识组织。该项目的长期目标是支持我们在超文本环境中对人类理解的科学理解的进步。 对超文本理解的进一步理解不仅将促进对人类认知的一般理解，而且将促进对设计更容易理解的网站的条件的理解。 该项目将为认知心理学家，网页设计师和人机交互专家提供观察内容呈现和领域知识对导航模式和生产数据的影响的新方法。 作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。