Data Visualization as Participatory Research: A Model for Digital Collections to Inspire User-Driven Research

The emerging creative practices of digital collections are increasing in prominence thanks to the access to, and appeal of, data visualizations. Interactive data visualizations, such as a map, timeline, chart, graph, or artist’s rendering of data, enable users to pursue their own research interests within a collection and participate in new areas of research. Use of these data visualizations enhances the scope of the collection and enables a modern and diverse thematic collection to emerge. This article considers the question: Does the addition of data visualizations affect how users participate with a research collection? Impacts on collection development, application of user-centered design, and analysis of users’ exploratory strategies are discussed. Examples of data visualizations used in digital humanities projects and digital collections are presented as case studies.     

Dealing with disagreement: The roles of topic familiarity and disagreement explanation in evaluation of conflicting expert claims and sources

Learners may increasingly encounter conflicting expert reports. However, little is known about how they deal with this challenge. We examined how learners' familiarity with a controversial historical topic affects their epistemic judgments of conflicting expert claims and sources, the interplay of their claim and source evaluation strategies, and their meta-epistemic understanding of the legitimacy of the disagreement (absolutist, multiplist, and evaluativist perspectives). In two studies, topic familiarity increased agreement with belief-consistent expert claims and the perceived trustworthiness of the expert who presented these claims. Topic familiarity also impacted the coordination of evaluation strategies and led to greater reliance on knowledge-based validation. However, topic familiarity did not affect meta-epistemic understanding of the legitimacy of the controversy. In the second study, reading an explanation about reasons for disagreements between historians resulted in higher evaluativism. Teaching about expert disagreement may be a productive approach for promoting appreciation of the diversity of knowledge.     

中职体育教学中学生团队意识的培养探析

当前,我国社会市场对人才的要求越来越多,而在众多要求中团队精神已经成为学生学习、求职、从事行业工作必备的条件。中职生是一群特殊的群体,其专业学习具有很强的职业性与目的性,在为中职生开展的体育教学中培养学生的团队意识非常重要。本文结合笔者多年的工作实践,探讨中职体育教学培养学生团队意识的策略,以供参考。     

协同搜索过程中用户交流内容与模式研究

[目的/意义]旨在分析协同搜索用户在信息搜索任务过程中的交流内容与模式,从而理解协同搜索用户的关注重点与搜索过程。[研究设计/方法]基于书籍交互检索平台(CLEF-Social Book Search)设计实验,共招募18名被试完成两种搜索任务,通过录音记录对话并对其进行编码和分析,总结交流内容特征和模式。结合任务类型、认知类型组合、服务器记录的搜索交互行为日志以及问卷收集的搜索体验进行了探索分析。[结论/发现]从交流内容上看,协同搜索用户主要理解与评判书目信息、商讨搜索任务计划;比起认知类型不同的用户,相同认知类型的用户在操作交互方面交流更多,在评判决策方面交流较少。交流模式依据讨论内容比重可分为理解评判型、评判主导型、均衡交流型三种,评判主导型用户的任务完成满意度最高。[创新/价值]协同搜索用户的交流反映出搜索过程中需要与同伴商讨协同的焦点,也是需要系统提供协助的重点,给协同搜索系统设计提供一定参考。本研究针对协同搜索的交流内容设计的编码系统对相关的协同交流研究也有借鉴意义。     

Beyond traditional collaborative search: Understanding the effect of awareness on multi-level collaborative information retrieval

Although there has been a great deal of research into Collaborative Information Retrieval (CIR) and Collaborative Information Seeking (CIS), the majority has assumed that team members have the same level of unrestricted access to underlying information. However, observations from different domains (e.g. healthcare, business, etc.) have suggested that collaboration sometimes involves people with differing levels of access to underlying information. This type of scenario has been referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, no studies have been conducted to investigate the effect of awareness, an existing CIR/CIS concept, on MLCIR. To address this gap in current knowledge, we conducted two separate user studies using a total of 5 different collaborative search interfaces and 3 information access scenarios. A number of Information Retrieval (IR), CIS and CIR evaluation metrics, as well as questionnaires were used to compare the interfaces. Design interviews were also conducted after evaluations to obtain qualitative feedback from participants. Results suggested that query properties such as time spent on query, query popularity and query effectiveness could allow users to obtain information about team's search performance and implicitly suggest better queries without disclosing sensitive data. Besides, having access to a history of intersecting viewed, relevant and bookmarked documents could provide similar positive effect as query properties. Also, it was found that being able to easily identify different team members and their actions is important for users in MLCIR. Based on our findings, we provide important design recommendations to help develop new CIR and MLCIR interfaces.     

我国球场观众暴力遏制策略的研究

采用文献资料、访谈与现场研究等方法探讨我国球场观众暴力的遏制策略问题。在介绍中外防范与治理球场观众暴力主要方法的基础上，针对我国球场观众暴力防治工作现状与问题，提出了6条遏制策略。研究结果可供政府有关部门在防治球场观众暴力时参考。     

远程开放教育环境下教师的导学策略及实施

本文围绕远程开放教育环境下教师如何增强导学功能展开探讨，并借用认知建构主义学派的观点，分别针对“为什么学习”、“能否学习”、“学习什么”、“如何学习”等四个问题来提出具体的导学策略。并在此基础上，进一步分析了远程开放教育环境下成功实施导学策略的条件。希望本研究能给教师们以实践启迪。     

A Corpus-Based Learning Method of Compound Noun Indexing Rules for Korean

In Korean information retrieval, compound nouns play an important role in improving precision in search experiments. There are two major approaches to compound noun indexing in Korean: statistical and linguistic. Each method, however, has its own shortcomings, such as limitations when indexing diverse types of compound nouns, over-generation of compound nouns, and data sparseness in training. In this paper, we propose a corpus-based learning method, which can index diverse types of compound nouns using rules automatically extracted from a large corpus. The automatic learning method is more portable and requires less human effort, although it exhibits a performance level similar to the manual-linguistic approach. We also present a new filtering method to solve the problems of compound noun over-generation and data sparseness.     

Measuring Search Engine Quality

The effectiveness of twenty public search engines is evaluated using TREC-inspired methods and a set of 54 queries taken from real Web search logs. The World Wide Web is taken as the test collection and a combination of crawler and text retrieval system is evaluated. The engines are compared on a range of measures derivable from binary relevance judgments of the first seven live results returned. Statistical testing reveals a significant difference between engines and high intercorrelations between measures. Surprisingly, given the dynamic nature of the Web and the time elapsed, there is also a high correlation between results of this study and a previous study by Gordon and Pathak. For nearly all engines, there is a gradual decline in precision at increasing cutoff after some initial fluctuation. Performance of the engines as a group is found to be inferior to the group of participants in the TREC-8 Large Web task, although the best engines approach the median of those systems. Shortcomings of current Web search evaluation methodology are identified and recommendations are made for future improvements. In particular, the present study and its predecessors deal with queries which are assumed to derive from a need to find a selection of documents relevant to a topic. By contrast, real Web search reflects a range of other information need types which require different judging and different measures.     

跨语言综合搜索引擎设计

分析了当前网络上信息分布的特点, 对目前相关的技术进行评价。在此基础上, 设计了一个解决网络搜索过程中语言障碍的系统——跨语言综合搜索引擎。