This chapter discusses methods for evaluating systems for remote guidance systems, particularly systems that use augmented reality (AR) technology to improve collaboration on physical task. It summarizes some of the systematic reviews that have been conducted on collaborative AR systems, and the evaluation measures used for AR systems. The chapter describes the experimental methods and data collection methods used and shows how to conduct evaluation of AR systems for remote guidance. It highlights directions for future research and new methods that could be used for evaluation of AR systems for remote guidance.
Recent research has indicated that human graph reading performance can be affected by the size of
crossing angles. The aesthetic of crossing angles is closely related to another aesthetic factor: edge
crossings. Although the number of edge crossings has been previously identified as the most important
aesthetic, its relative impact on human graph reading, compared to the size of crossing angles, has
not been investigated. In this paper, we present an exploratory user study investigating the relative
importance between crossing number and crossing angle. This study also aims to further examine the
effects of crossing number and crossing angle not only on task performance measured as response
time and accuracy, but also on cognitive load and visualization efficiency. The experimental results
reinforce the previous findings that the two aesthetics each significantly affect performance of human
graph reading. Further, in terms of the relative importance, the study demonstrates that given the current
setting of the user study, the number of edge crossings is relatively more important than the size of
crossing angles. To be more specific, crossing number and crossing angle together explain about 40%
of the variance in response time, mental effort and visualization efficiency, with about 83% of the
explained variance being attributed to crossing number. In regard to response accuracy, crossing number
and crossing angle together explain about 14% of the variance, with a slightly larger portion of the
explained variance being attributed to crossing number.
Recent research has indicated that human graph reading performance can be affected by the size of crossing angle. Crossing angle is closely related to another aesthetic criterion: number of edge crossings. Although crossing number has been previously identified as the most important aesthetic, its relative impact on performance of human graph reading is unknown, compared to crossing angle. In this paper, we present an exploratory user study investigating the relative importance between crossing number and crossing angle. This study also aims to further examine the effects of crossing number and crossing angle not only on task performance measured as response time and accuracy, but also on cognitive load and visualization efficiency. The experimental results reinforce the previous findings of the effects of the two aesthetics on graph comprehension. The study demonstrates that on average these two closely related aesthetics together explain 33% of variance in the four usability measures: time, accuracy, mental effort and visualization efficiency, with about 38% of the explained variance being attributed to the crossing angle.
How we visualize graph data is important for us to make sense of it. A number of aesthetic criteria have been used in practice to guide the visualization process and judge the quality of graph drawings. These aesthetics are limited since they often conflict with each other. It is generally agreed that in order to make visualizations effective, well-grounded perception and cognitive theories and design principles are needed. Some attempts have been made to develop visualization theories. In this paper, we present a preliminary study which we conducted with a cognitive approach to add to this growing body of research. More specifically, we propose a graph visualization model, which is further conceptualized into a two-stage assessment cycle. Examples of potentially useful methodologies and theories are introduced and their implications for producing user-friendly visualizations are discussed.
Force-directed algorithms are widely used in practice for graph drawing. How to evaluate this type of algorithms has been a challenging issue since their performance largely depends on input parameters and thus is not consistent. In this paper, we first review previous approaches used for evaluation of force-directed algorithms. We then present a case study that compares two force-directed algorithms following a newly proposed evaluation framework. This study evaluates the performance of these algorithms in terms of six commonly applied aesthetic criteria and demonstrates how the framework is used. Advantages of this evaluation framework are discussed.
Graphs are often drawn into straight-line node-link diagrams for better understanding of the underlying data. However, curves have also been used in graph visualization for various purposes. Orthogonal drawing is one type of graph drawings in which each edge is made up with an alternating sequence of vertical and horizontal line segments. In the past, many algorithms have been developed for orthogonal drawing so that the resulting drawings meet some pre-defined aesthetic criteria and constraints. Experiments have also been conducted to evaluate performance of algorithms and effectiveness of orthogonal drawings. In this paper, we briefly summarize the research that has been done in relation to orthogonal drawing and provide directions for future research.
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