From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Express: a web-based technology to support human and computational experimentation

Experimental cognitive psychology has been greatly assisted by the development of general computer-based experiment presentation packages. Typically, however, such packages provide little support for running participants on different computers. It is left to the experimenter to ensure that group sizes are balanced between conditions and to merge data gathered on different computers once the experiment is complete. Equivalent issues arise in the evaluation of parameterized computational models, where it is frequently necessary to test a model's behavior over a range of parameter values (which amount to between-subjects factors) and where such testing can be speeded up significantly by the use of multiple processors. This article describes Express, a Web-based technology for coordinating "clients" (human participants or computational models) and collating client data. The technology provides an experiment design editor, client coordination facilities (e.g., automated randomized assignment of clients to groups so that group sizes are balanced), general data collation and tabulation facilities, a range of basic statistical functions (which are constrained by the specified experimental design), and facilities to export data to standard statistical packages (such as SPSS). We report case studies demonstrating the utility of Express in both human and computational experiments. Express may be freely downloaded from the Express Web site (http://express.psyc.bbk.ac.uk/)

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net

EPOS : evolving personal to organizational knowledge spaces

EPOS will leverage the user´s personal workspace with its manyfold native information structures to his personal knowledge space and in cooperation with other personal workspaces contribute to the organizational knowledge space which is represented in the organizational memory. This first milestone presents results from the project´s first year in the areas of the personal informational model, user observation for context elicitation, collaborative information retrieval and information visualization

The matrix revisited: A critical assessment of virtual reality technologies for modeling, simulation, and training

A convergence of affordable hardware, current events, and decades of research have advanced virtual reality (VR) from the research lab into the commercial marketplace. Since its inception in the 1960s, and over the next three decades, the technology was portrayed as a rarely used, high-end novelty for special applications. Despite the high cost, applications have expanded into defense, education, manufacturing, and medicine. The promise of VR for entertainment arose in the early 1990\u27s and by 2016 several consumer VR platforms were released. With VR now accessible in the home and the isolationist lifestyle adopted due to the COVID-19 global pandemic, VR is now viewed as a potential tool to enhance remote education. Drawing upon over 17 years of experience across numerous VR applications, this dissertation examines the optimal use of VR technologies in the areas of visualization, simulation, training, education, art, and entertainment. It will be demonstrated that VR is well suited for education and training applications, with modest advantages in simulation. Using this context, the case is made that VR can play a pivotal role in the future of education and training in a globally connected world

Visualisation of Interactions in Online Collaborative Learning Environments

Much research in recent years has focused on the introduction of ‘Virtual Learning Environments’ (VLE’s) to universities, documenting practice and sharing experience. Communicative tools are the means by which VLE’s have the potential to transform learning with computers from being passive and transmissive in nature, to being active and constructivist. Attention has been directed towards the importance of online dialogue as a defining feature of the VLE. However, practical methods of reviewing and analysing online communication to encode and trace cycles of real dialogue (and learning) have proved somewhat elusive. Qualitative methods are under-used for VLE discussions, since they demand new sets of research skills for those unfamiliar with those methods. Additionally, it can be time-intensive to learn them. This thesis aims to build an improved and simple-to-use analytical tool for Moodle that will aid and support teachers and administrators to understand and analyse interaction patterns and knowledge construction of the participants involved in ongoing online interactions. After reviewing the strengths and shortcomings of the existing visualisation models, a new visualisation tool called the Virtual Interaction Mapping System (VIMS) is proposed which is based on a framework proposed by Schrire (2004) to graphically represent social presence and manage the online communication patterns of the learners using Moodle. VIMS produces multiple possible views of interaction data so that it can be evaluated from many perspectives; it can be used to represent interaction data both qualitatively and quantitatively. The units of analysis can be represented graphically and numerically for more extensive evaluation. Specifically, these indicators are communication type, participative level, meaningful content of discussion, presence of lurkers, presence of moderators, and performance of participants individually and as a group. It thus enables assessment of the triangular relationship between conversationcontent, online participation and learnin

Using Categorical Information in Multidimensional Data Sets: Interactive Partition and Cluster Comparison

Multidimensional data sets often include categorical information. When most columns have categorical information, clustering the data set by similarity of categorical values can reveal interesting patterns in the data set. However, when the data set includes only a small number (one or two) of categorical columns, the categorical information is probably more useful as a way to partition the data set. For example, researchers might be interested in gene expression data for healthy vs. diseased patients or stock performance for common, preferred, or convertible shares. For these cases, we present a novel way to utilize the categorical information together with clustering algorithms. Instead of incorporating categorical information into the clustering process, we can partition the data set according to categorical information. Clustering is then performed with each subset to generate two or more clustering results, each of which is homogeneous (i.e. only includes the same categorical value for the categorical column). By comparing the partitioned clustering results, users can get meaningful insights into the data set: users can identify an interesting group of items that are differentially/similarly expressed in two different homogeneous partitions. The partition can be done in two different directions: (1) by rows if categorical information is available for each column (e.g. some columns are from disease samples and other columns are from healthy samples) or (2) by a column if a column contains categorical information (e.g. a column represents a categorical attribute such as colors or sex). We designed and implemented an interface to facilitate this interactive partition-based clustering results comparison. Coordination between clustering results displays and comparison results overview enables users to identify interesting clusters, and a simple grid display clearly reveals correspondence between two clusters