jGRASP has three integrated approaches for interacting with its dynamic viewers for data structures: debugger, workbench, and text-based interactions that allow individual Java statements and expressions to be executed/evaluated. These approaches can be used together to provide a complementary set of interactions with the dynamic viewers. Data structure identification and rendering were tested by examining examples from 20 data structure textbooks. Controlled experiments with CS2 students indicate that the viewers can have a significant positive impact on student performance. The overall result is a flexible environment for interacting with effective dynamic data structure visualizations generated by a robust structure identifier.

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The jGRASP IDE has been extended to provide object viewers that automatically generate dynamic, state-based visualizations of data structures in Java.These viewers provide multiple synchronized visualizations of data structures as the user steps through the source code in either debug or workbench mode. This tight integration in a lightweight IDE provides a unique and promising environment for learning data structures.The jGRASP structure identifier, which is used to automatically identify data structure objects and generate appropriate visualizations, has been tested with examples from 20 CS2 data structure textbooks. The results of the testing indicate that the structure identifier is extremely robust.

The purpose of this pilot study was to explore the feasibility of using hand drawn images to identify symbol components for incorporation into warning symbol design software. This software will use an interactive evolutionary computation (IEC) algorithm to generate and evolve symbols mathematically described by a set of numerical parameters. Therefore, participants (N = 100) ages 19–43 (x = 23.2) were recruited to determine these symbol design parameters. Participants were invited to hand draw warning symbols for three referents: fall from elevation, hearing protection, and hazardous atmosphere. A panel of design engineers determined 27 attributes were present in the fall from elevation, 19 in the hearing protection, and 25 in the hazardous atmosphere images. A direct clustering algorithm was used to determine which attributes, or symbol parameters, were most commonly present or conspicuously absent among the clustered image families. For the fall from elevation, hearing protection and hazardous atmosphere referents, the clustering algorithm identified six, four and four symbol parameters, respectively, primarily responsible for distinguishing one drawn symbol from another. Thus, these parameters will be included as evolvable genes in the IEC software.

The Montgomery County Corrections Program is a program designed to address the problem of overcrowded jails by providing an out-of-jail rehabilitative program as an alternative. The candidate offenders chosen for this program are offenders convicted on nonviolent charges and are currently chosen subjectively with little statistical basis. In addition, historical data has been recorded on offenders who have passed through the program, making the program a good candidate for case-based reasoning. Using such reasoning, County Officials would like an objective measurement which will predict the success or failure of a candidate offender based on past offender history. The four case-based reasoning algorithms chosen for this prediction are Discrete, Continuous and Distance Weighted k-Nearest Neighbors and a General Regression Neural Network (GRNN). Although all four algorithms prove to be an improvement on the current system, the GRNN performs the best, with an average accuracy rate of 68%.

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