214 research outputs found
Knowledge Cartography for Open Sensemaking Communities
Knowledge Cartography is the discipline of visually mapping the conceptual structure of ideas, such as the connections between issues, concepts, answers, arguments and evidence. The cognitive process of externalising one's understanding clarifies one's own grasp of the situation, as well as communicating it to others as a network that invites their contributions. This sensemaking activity lies at the heart of the Open Educational Resources movement's objectives. The aim of this paper is to describe the usage patterns of Compendium, a knowledge mapping tool from the OpenLearn OER project, using quantitative data from interaction logs and qualitative data from knowledge maps, forums and blog postings. This work explains nine roles played by maps in OpenLearn, and discusses some of the benefits and adoption obstacles, which motivate our ongoing work
Data Visualization for Network Simulations
As many other kinds of simulation experiments, simulations of computer networks tend to generate high volumes of output data. While the collection and the statistical processing of these data are challenges in and of themselves, creating meaningful visualizations from them is as much an art as it is a science. A sophisticated body of knowledge in information design and data visualization has been developed and continues to evolve. However, many of the visualizations created by the network simulation community tend to be less than optimal at creating compelling, informative narratives from experimental output data. The primary contribution of this paper is to explore some of the design dimensions in visualization and some advances in the field that are applicable to network simulation. We also discuss developments in the creation of the visualization subsystem in the Simulation Automation Framework for Experiments (SAFE) in the context of best practices for data visualization
Visual Analytics for Understanding Draco's Knowledge Base
Draco has been developed as an automated visualization recommendation system
formalizing design knowledge as logical constraints in ASP (Answer-Set
Programming). With an increasing set of constraints and incorporated design
knowledge, even visualization experts lose overview in Draco and struggle to
retrace the automated recommendation decisions made by the system. Our paper
proposes an Visual Analytics (VA) approach to visualize and analyze Draco's
constraints. Our VA approach is supposed to enable visualization experts to
accomplish identified tasks regarding the knowledge base and support them in
better understanding Draco. We extend the existing data extraction strategy of
Draco with a data processing architecture capable of extracting features of
interest from the knowledge base. A revised version of the ASP grammar provides
the basis for this data processing strategy. The resulting incorporated and
shared features of the constraints are then visualized using a hypergraph
structure inside the radial-arranged constraints of the elaborated
visualization. The hierarchical categories of the constraints are indicated by
arcs surrounding the constraints. Our approach is supposed to enable
visualization experts to interactively explore the design rules' violations
based on highlighting respective constraints or recommendations. A qualitative
and quantitative evaluation of the prototype confirms the prototype's
effectiveness and value in acquiring insights into Draco's recommendation
process and design constraints.Comment: To be presented at VIS 202
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