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While there is growing demand for use of climate model projections to understand the potential impacts of future climate on resources, there is a lack of effective visuals that convey the range of possible climates across spatial scales and with uncertainties that potential users need to inform their impact assessments and studies. We use usability testing including eye tracking to explore how a group of resource professionals (foresters) interpret and understand a series of graphical representations of future climate change, housed within a web-based decision support system (DSS), that address limitations identified in other tools. We find that a three-map layout effectively communicates the spread of future climate projections spatially, that location-specific information is effectively communicated if depicted both spatially on a map and temporally on a time series plot, and that model error metrics may be useful for communicating uncertainty and in demonstrating the utility of these future climate datasets.

Decision support systems—collections of related information located in a central place to be used for decision-making—can be used as platforms from which climate information can be shared with decision-makers. Unfortunately, these tools are not often evaluated, meaning developers do not know how useful or usable their products are. In this study, a web-based climate decision support system (DSS) for foresters in the southeastern United States was evaluated by using eye-tracking technology. The initial study design was exploratory and focused on assessing usability concerns within the website. Results showed differences between male and female forestry experts in their eye-tracking behavior and in their success with completing tasks and answering questions related to the climate information presented in the DSS. A follow-up study, using undergraduate students from a large university in the southeastern United States, aimed to determine whether similar gender differences existed and could be detected and, if so, whether the cause(s) could be determined. The second evaluation, similar to the first, showed that males and females focused their attention on different aspects of the website; males focused more on the maps depicting climate information while females focused more on other aspects of the website (e.g., text, search bars, and color bars). DSS developers should consider the possibility of gender differences when designing a web-based DSS and include website features that draw user attention to important DSS elements to effectively support various populations of users.

Spatial thinking is defined by Uttal et al. (Psychological Bulletin 139:352–402, 2013) as, “the mental processes of representing, analyzing, and drawing inferences from spatial relations…between objects…or…within objects” (p. 367). There are many spatial skills, and different disciplines and fields have varying names and definitions for them.

Communicating climate information is challenging due to the interdisciplinary nature of the topic along with compounding cognitive and affective learning challenges. Graphs are a common representation used by scientists to communicate evidence of climate change. However, it is important to identify how and why individuals on the continuum of expertise navigate graphical data differently as this has implications for effective communication of this information. We collected and analyzed eye-tracking metrics of geoscience graduate students and novice undergraduate students while viewing graphs displaying climate information. Our findings indicate that during fact-extraction tasks, novice undergraduates focus proportionally more attention on the question, title and axes graph elements, whereas geoscience graduate students spend proportionally more time viewing and interpreting data. This same finding was enhanced during extrapolation tasks. Undergraduate novices were also more likely to describe general trends, while graduate students identified more specific patterns. Undergraduates who performed high on the pre-test measuring graphing skill, viewed graphs more similar to graduate students than their peers who performed lower on the pre-test.

Augmented reality (AR) sandboxes are useful tools in helping students develop a three-dimensional understanding of topographic maps and landforms. Research on the AR sandbox indicates that students enjoy it, but evidence about student learning gains is inconclusive. This study aims to understand how students engage with these tools as a precedent to understanding learning gains. Technology in the form of skin biosensors allows researchers to measure skin conductance (or electrodermal activity – EDA) as a proxy for student engagement. This study used wrist biosensors to monitor the engagement of students exposed to different instructional treatments of an AR sandbox in an introductory lab course. The data were supplemented by dynamic video of the students interacting with the sandbox. Statistical analysis of the EDA data suggests that the use of an AR sandbox, particularly during structured lab activities, increases student engagement. Regression modeling supports a relationship between spatial reasoning skills. Based on video data, students’ collaborative and social interactions also showed a significant relationship with EDA. These results suggest that instructors focus on developing and using structured and collaborative activities when incorporating AR sandboxes in their courses in order to best engage students.

The approach is outlined for the development of an expert system for the -preliminary -design-of-bridges-(-RCAB£). using.lhe exp.ert system "shell, EXSYS, running under MS-DOS on an IBM-PC. After some consideration of the preliminary design process and the relevant design rules, the strategy for the development of the expert system is discussed. This was based on a series of knowledge sources integrated through a master knowledge source, with an exchange of data through a common data base. This approach, which fully uses the features of EXSYS, was found to be most satisfactory since each knowledge source could be tested and developed independently.