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Modern computer graphics courses require students to complete assignments involving computer programming. The evaluation of student programs, either by the student (self-assessment) or by the instructors (grading) can take a considerable amount of time and does not scale well with large groups. Interactive judges giving a pass/fail verdict do constitute a scalable solution, but they only provide feedback on output correctness. In this article, we present a tool to provide extensive feedback on student submissions. The feedback is based both on checking the output against test sets, as well as on syntactic and semantic analysis of the code. These analyses are performed through a set of code features and instructor-defined rubrics. The tool is built with Python and supports shader programs written in GLSL. Our experiments demonstrate that the tool provides extensive feedback that can be useful to support self-assessment, facilitate grading, and identify frequent programming mistakes. [ABSTRACT FROM AUTHOR]

In an effort to develop visualization analysis and design skills in master's level information visualization students, the authors use a well-known analytical usability evaluation method, heuristic evaluation, with different sets of heuristics to teach students to analyze visualization applications. The proposed approach, used for three consecutive years, has successfully stimulated critical analysis and discussion sessions as well as helped raise students' awareness concerning the benefit of using systematic analysis methods and the strategies and guidelines that should be used to design visualization applications. [ABSTRACT FROM AUTHOR]

Craniometric analysis plays an important role in anthropology studies and forensics. This paper presents CraMs, an application using a new craniometric approach based on 3D models of the skull. The main objective is to obtain, through a process supervised by anthropologists, the main points of interest used to compute craniometric measurements. The application aids this process by analyzing the skull geometry and automatically providing points of interest. The application also allows for semiautomatic point detection, where the user provides an initial guess that might be refined based on the curvature of the skull, as well as the manual selection of any other points of interest. Moreover, results comparing measurements obtained with CraMs and traditional craniometry methods on eight skulls suggest that the application provides comparable craniometric measurements and lower inter-observer variability. This approach offers advantages such as an easier access to skulls with no risk of bone damage and the possibility of defining new measurements based on morphology or other skull characteristics, which are not possible using traditional methods. [ABSTRACT FROM PUBLISHER]