R. Jordan Crouser

Comparing the use of tangible and graphical programming languages for informal science education

Much of the work done in the field of tangible interaction has focused on creating tools for learning; however, in many cases, little evidence has been provided that tangible interfaces offer educational benefits compared to more conventional interaction techniques. In this paper, we present a study comparing the use of a tangible and a graphical interface as part of an interactive computer programming and robotics exhibit that we designed for the Boston Museum of Science. In this study, we have collected observations of 260 museum visitors and conducted interviews with 13 family groups. Our results show that visitors found the tangible and the graphical systems equally easy to understand. However, with the tangible interface, visitors were significantly more likely to try the exhibit and significantly more likely to actively participate in groups. In turn, we show that regardless of the condition, involving multiple active participants leads to significantly longer interaction times. Finally, we examine the role of children and adults in each condition and present evidence that children are more actively involved in the tangible condition, an effect that seems to be especially strong for girls.

Tangible interaction and learning: the case for a hybrid approach

Research involving tangible interaction and children has often focused on how tangibles might support or improve learning compared to more traditional methods. In this paper, we review three of our research studies involving tangible computer programming that have addressed this question in a variety of learning environments with a diverse population of children. Through these studies, we identify situations in which tangible interaction seems to offer advantages for learning; however, we have also identify situations in which tangible interaction proves less useful and an alternative interaction style provides a more appropriate medium for learning. Thus, we advocate for a hybrid approach—one that offers teachers and learners the flexibility to select the most appropriate interaction style to meet the needs of a specific situation.

Two Visualization Tools for Analyzing Agent-Based Simulations in Political Science

Agent-based modeling has become a key technique for modeling and simulating dynamic, complicated behaviors in the social and political sciences. Although many robust toolkits for developing and running these simulations exist, systems that support analysis of their results are few and tend to be overly general. So, social scientists have had difficulty interpreting the results of their increasingly complex simulations. To help bridge this gap between data generation and interpretation, researchers collaborated with political science analysts to design two tools for interactive data exploration and domain-specific data analysis. Testing by the analysts validated that these tools provided an efficient framework to explore individual trajectories and the relationships between variables. The tools also supported hypothesis generation by enabling analysts to group simulations according to multidimensional similarity and drill down to investigate further.

Balancing Human and Machine Contributions in Human Computation Systems

Many interesting and successful human computation systems leverage the complementary computational strengths of both humans and machines to solve these problems. In this chapter, we examine Human Computation as a type of Human-Computer Collaboration—collaboration involving at least one human and at least one computational agent. We discuss recent advances in the open area of function allocation, and explore how to balance the contributions of humans and machines in computational systems. We then explore how human-computer collaborative strategies can be used to solve problems that are difficult or computationally infeasible for computers or humans alone.

Manipulating and controlling for personality effects on visualization tasks

Researchers in human–computer interaction and visualization have recently been challenged to develop a better understanding of users’ underlying cognitive processes in order to improve system design and evaluation. While existing studies lay a critical foundation for understanding the role of cognitive processes and individual differences in visualization, concretizing the intuition that each user experiences a visual interface through an individual cognitive lens is only half the battle. In this article, we investigate the impact of manipulating users’ personality on observed behavior when using a visualization. In a targeted study, we demonstrate that personality priming can result in changes in behavior when interacting with visualizations. We then discuss how this and similar techniques could be used to control for personality effects when designing and evaluating visualizations systems.

Exploring hierarchical visualization designs using phylogenetic trees

Ongoing research on information visualization has produced an ever-increasing number of visualization designs. Despite this activity, limited progress has been made in categorizing this large number of information visualizations. This makes understanding their common design features challenging, and obscures the yet unexplored areas of novel designs. With this work, we provide categorization from an evolutionary perspective, leveraging a computational model to represent evolutionary processes, the phylogenetic tree. The result - a phylogenetic tree of a design corpus of hierarchical visualizations - enables better understanding of the various design features of hierarchical information visualizations, and further illuminates the space in which the visualizations lie, through support for interactive clustering and novel design suggestions. We demonstrate these benefits with our software system, where a corpus of two-dimensional hierarchical visualization designs is constructed into a phylogenetic tree. This software system supports visual interactive clustering and suggesting for novel designs; the latter capacity is also demonstrated via collaboration with an artist who sketched new designs using our system.

Exploring agent-based simulations in political science using Aggregate Temporal Graphs

Agent-based simulation has become a key technique for modeling and simulating dynamic, complicated behaviors in social and behavioral sciences. As these simulations become more complex, they generate an increasingly large amount of data. Lacking the appropriate tools and support, it has become difficult for social scientists to interpret and analyze the results of these simulations. In this paper, we introduce the Aggregate Temporal Graph (ATG), a graph formulation that can be used to capture complex relationships between discrete simulation states in time. Using this formulation, we can assist social scientists in identifying critical simulation states by examining graph substructures. In particular, we define the concept of a Gateway and its inverse, a Terminal, which capture the relationships between pivotal states in the simulation and their inevitable outcomes. We propose two real-time computable algorithms to identify these relationships and provide a proof of correctness, complexity analysis, and empirical run-time analysis. We demonstrate the use of these algorithms on a large-scale social science simulation of political power and violence in present-day Thailand, and discuss broader applications of the ATG and associated algorithms in other domains such as analytic provenance.

Exploring agent-based simulations using temporal graphs

Agent-based simulation has become a key technique for modeling and simulating dynamic, complicated behaviors in social and behavioral sciences. Lacking the appropriate tools and support, it is difficult for social scientists to thoroughly analyze the results of these simulations. In this work, we capture the complex relationships between discrete simulation states by visualizing the data as a temporal graph. In collaboration with expert analysts, we identify two graph structures which capture important relationships between pivotal states in the simulation and their inevitable outcomes. Finally, we demonstrate the utility of these structures in the interactive analysis of a large-scale social science simulation of political power in present-day Thailand.

Computational exploration of simulations in political science

Agent-based simulation (ABS) has become an important tool for studying complicated group behaviors in social science.1 In ABS, large numbers of autonomous entities, or ‘agents,’ interact with one another and, over time, they influence and are influenced by the agents around them. Given that these simulations are stochastic, that is, they use small random perturbations, they generally have to be run hundreds of times to generate a distribution of sample behavioral patterns. Increased computing power enables scientists to simulate and study increasingly complex systems. For example, ABS is being used to model cooperative behavior,2 ethnic mobilization and conflict,3 violence and genocide,4 and population growth and collapse.5 Since these systems are being used to simulate elaborate patterns of human behavior, they require thousands of agents each with a large number of variables to direct its actions and interactions with those around it. Such complexity means that these systems often generate gigabytes of raw data for each simulation. Unfortunately, these very large data sets can prove incredibly costly to interpret and analyze. Lacking appropriate tools and support, scientists studying these systems are driven to oversimplify their models or perform purely numerical analyses that, by design, overlook many of the subtle yet important forces driving behaviors. To get around this issue, we reframed this process as a graph exploration problem. In other words, we began by asking questions about which configurations in the simulation can be reached from a given starting position. In this context, a graph is a representation of objects (called vertices) and the relationships between those objects. Vertices are unique configurations of the variables controlling the simulation, and the relationship between two configurations is a transition in time. This reframing enables us to employ concepts from graph theory to describe behavioral patterns in these data sets. Figure 1. Visualization of an aggregate temporal graph generated from 100 runs of an agent-based simulation of political hierarchies. The two yellow vertices represent an interesting feature: a highly stable vertex pair (note the high degree of revisitation).