Clarity from Confusion: Using Intended Interactions to Design Information Systems

Two tools are described that help designers visualize the structure of a system in the requirements phase of a project. First, a matrix is constructed that represents the tendency of components to interact. The matrix is derived from sequence diagrams, which in turn are based on textual scenarios. This interaction matrix is transformed into a structure plot of the system, showing a graph of the essential connections between actors. Second, this same matrix is used to generate a sequence plot: a sequence diagram optimized for problem-solving. We illustrate the effectiveness of this approach, first with a simulation study, and later with a participant-based study of inference from diagrams. The results suggest that a similarity-based approach to information systems design can generate new testable tools. Pragmatically, the tools help novices and experts alike by automatically generating candidate system configurations in the form of structural diagrams, and by generating better sequence diagrams

The Spatial Nature of Thought: Understanding Systems Design Through Diagrams

Design entails the interaction of minds and the tools used to express the design, notably, diagrams. Systems designers use the affordances of the page when they generate structural diagrams of systems. Specifically, they use proximity to augment connectedness (path) information by grouping subsystems. They use horizontal position on the page to express sequence and vertical position to reflect actual spatial position. Finally, they use the permanence of diagrams to generate alternative designs. These conclusions were reached through the analysis of work by student designers, many of whom were practicing information technology professionals. The analysis of designs in topological and Euclidean space required the creation of computational tools that show promise as decision aids for designers, by separating the intertwined qualities of topological and Euclidean space, and by making visible the conceptual similarity of design alternatives

Matching Mechanisms to Situations Through the Wisdom of the Crowd

Designing a system often begins with matching existing solutions to current problems. Specifically, integration mechanisms are mapped onto situations. Novices are not good at this task, and experts are rare. Could crowdsourcing, that is, aggregating the suggestions of individuals working independently, be effective? Two experiments, one with design students in a classroom, and another with participants on the web, demonstrated that the crowd possesses wisdom about how to match mechanisms to situations. Participants also categorized situations, and those who name their categories were better at matching than those who didn’t. The results have pragmatic implications, suggesting it is possible to crowdsource design, and providing new ways of eliciting, testing, and training expertise. More generally, the paper suggests a new model for information system design based on analogical mapping

Making the Implicit Explicit:Effects of Verbalization in Decisions from Experience

What do people learn from experience with repeated decisions?Is it merely implicit behavioral tendencies? If so, wouldarticulating or summarizing what is learned change behavior?Online participants (N=126) experienced 100 trials of adecisions-from-experience problem with outcome feedback.Some participants then verbally summarized what they hadlearned and estimated the probability of the risky gain eitherfor themselves (Self condition) or for another hypotheticalplayer (Other condition); others did not summarize (Controlcondition). Finally, they faced 20 more decision trials.Verbalizing a social message to another person significantlyincreased sure choices (that is, decreased risk-taking) insubsequent decision making. In general, participantsunderestimated the probabilities of both certain and riskyprospects, and articulating a summary message (Self or Other)seemed to increase this conservatism

Visual Similarity Modeling of Chinese Characters Across Natives, Second Language Learners, and Novices

This study investigated how well similarity models of Chinese characters developed in previous research could be used to model human judgments across different levels of proficiency in Chinese. The behavioral data collected from the three groups of participants confirmed the superiority of and preference for configurations over components in experts' perceptions. In contrast, Chinese learners' and novices' criteria for similarity judgments were less clear, as indicated by the low proportion of variance that could be accounted for by extended tree analysis of their group judgments. We discuss computational challenges in modeling human perception and judgments about Chinese characters and propose future directions for research, including the potential use of statistical and machine learning techniques with larger datasets for improved model development

Describing and Comprehending Change in Quantitative Information

We investigate how people understand English text that describes changes in a numeric quantity over time. We hypothesizethat people find it easier to comprehend text that specifies the starting quantity and ending quantity in chronological order,in contrast to how some news media tend to report this type of information, stating the ending quantity first, presumablybecause the ending quantity is the ”news”. Our hypothesis is that it is more difficult for readers to comprehend a sentencepresenting quantities in reverse chronological order, requiring more processing time by the reader and leading to reducedaccuracy in answering follow-up questions about the quantities. The results of an experiment supported the hypothesis.This finding has theoretical implications for models of text comprehension, and practical implications for how to commu-nicate technical material in newspapers, educational texts teaching or requiring the use of quantitative information, andtests and assessments based on reading passages