Low-Fidelity Algorithm Visualization

Abstract

Computer science educators have traditionally used algorithm visualization (AV) software to create graphical representations of algorithms for use as visual aids in lectures, or as the basis for interactive labs. Typically, such visualizations are high-fidelity in the sense that (a) they depict the target algorithm for arbitrary input, and (b) they tend to have the polished look of textbook figures. In contrast, low-fidelity visualizations illustrate the target algorithm for a few, carefully chosen input data sets, and tend to have a sketched, unpolished appearance. Drawing on ethnographic field studies of a junior-level undergraduate algorithms course, we motivate the use of low-fidelity AV technology as the basis for an alternative learning paradigm in which students construct their own visualizations, and then present those visualizations to their instructor and peers for feedback and discussion. To explore the design space of low-fidelity AV technology, we present SALSA (Spatial ALgorithmic Language for StoryboArding) and ALVIS (ALgorithmVI sualization Storyboarder), a prototype end-user language and system firmly rooted in empirical studies in which students constructed and presented visualizations made out of simple art supplies. Our prototype end-user language and system pioneer a novel technique for programming of visualizations based on spatial relations, and a novel presentation interface that supports human discussions about algorithms by enabling reverse execution and dynamic mark-up and modification. Moreover, the prototype provides an ideal foundation for what we see as the algorithms classroom of the future: the interactive ‘algorithms studio’.