A number of prior studies have found that using animation to help teach algorithms had less beneficial effects on learning than hoped. Those results surprise many computer science instructors whose intuition leads them to believe that algorithm animations should assist instruction. This article reports on a study in which animation is utilized in more of a "homework" learning scenario rather than a "final exam" scenario. Our focus is on understanding how learners will utilize animation and other instructional materials in trying to understand a new algorithm, and on gaining insight into how animations can fit into successful learning strategies. The study indicates that students use sophisticated combinations of instructional materials in learning scenarios. In particular, the presence of algorithm animations seems to make a challenging algorithm more accessible and less intimidating, thus leading to enhanced student interaction with the materials and facilitating learning. Keywords:...

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 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 (ALgorithm VIsualization 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."

Despite the intuitively compelling adage "a picture is worth a thousand words," attempts over the past decade to use animations to explain algorithms to students have produced disappointing results. In most cases interesting algorithm animations were designed, but no formal, systematic evaluations were conducted. When such evaluations were performed the results were mixed, with compelling evidence for the instructional superiority of algorithm animations failing to emerge. It is in this context that we embarked on a research program to develop educationally effective algorithm visualizations. This program was based on the premise that animations needed to be embedded in a knowledge and context providing hypermedia environment in order to effectively harness their power to enhance learning. This paper describes the architecture of the resulting Hypermedia Algorithm Visualization system HalVis. Four empirical studies with HalVis are described, which demonstrated that the extent of learning exhibited by students who used HalVis was significantly greater than that of students who used means of traditional instruction or a typical algorithm animation.

this article consolidates all of the meta-studys principal data for easy access. Readers interested in further scrutinizing the numerical counts that are graphed and discussed in Sections 3 and 4 can consult the appendix, which indicates the precise manner in which each of the SV effectiveness studies is classified

Computer science educators have traditionally used algorithm visualization (AV) software to create graphical representations of algorithms that are later used as visual aids in lectures, or as the basis for interactive labs.

Software design is a realm of messy or “wicked ” problems that are often too big, too ill-defined, and too complex for easy comprehension and solution (DeGrace and Stahl, 1998). Software itself is created, complex, abstract, and difficult to observe. Software is different from created physical artefacts, because it lacks their tangibility and visibility (e.g., What does a compiler look like? What is

this paper, I introduce Cultural Consensus Theory, a formalized, consensus-based model of community that has evolved out of research in cognitive anthropology. Using this theoretical framework as a guide, I develop an empirical method for measuring individuals level of agreement on tasks that involve the interpretation and construction of community representations. I illustrate the application of the method by presenting an example from my own research into the use of graphical representations in computer science education. Many questions still remain with respect to the practicality and mechanics of the methodquestions that only further empirical studies can answer. Non etheless, I hope to make the case here that the method holds promise for social constructivist-minded educators looking for richer, more valid ways of measuring learning than traditional knowledge testing. 2 Cultural Consensus Theory Over the past two decades, a line of research in cognitive anthropology has been concerned with formalizing a definition of culture based on consensus. The theory that has evolved out of that research Cultural Consensus Theory (see, e.g., Boster, 1985; Romney, Weller, & Batchelder, 1986; Weller & Romney, 1988)provides a suitable foundation both for determining whether a community of practice actually exists, and for assessing ones level of membership in such a community. According to Consensus Theory, each [community of practice] may be thought of as having an associated semantic domain that provides a way of classifying and talking about the elements in the culture pattern ( Romney, Weller, & Batchelder, 1986, p. 315). One can think of a semantic domain as an organized set of symbols (including notations, words, and graphical representations) for referring to a common c...

To explore how people conceptualize a complex system, 232 university students were asked to sketch how a search engine works. While the sketches reveal a diverse range of visual and conceptual approaches, a subset of the sketches exhibit an underlying regularity for describing algorithmic processes. To explain this regularity, I propose the conceptual metaphor: A SEARCH ENGINE IS A SERIES OF TEXT TRANSFORMATIONS and describe a set of mappings from sketchable graphic markings to abstractions in the search engine domain. I believe that this metaphor can be applied to enable people to more effectively conceptualize, describe, and explore complex systems. 1.

Empirical studies of novice programming typically rely on code solutions or test responses as the basis of their analyses. While such data can provide insight into novice programming knowledge, they say little about the programming processes in which novices engage. For those interested in improving novice programming environments, a key research question arises: How can we collect and analyze data on novice programming that will enable us (a) to analyze and compare the programming processes promoted by alternative novice programming environments, and (b) ultimately to build better novice programming environments? To address this question, we have collected a large video corpus of novices as they construct code solutions in various versions of ALVIS Live! [14], a novice programming environment. Through detailed post-hoc analyses of our video corpus, we have developed a methodology for compiling the moment-by-moment evolution of novice code solutions. Based on an analysis of an ideal code solution’s key semantic components, our methodology enables one to document, on a second-by-second basis, (a) what part of a code solution a programmer is focusing on, and (b) where the semantic feedback provided by the programming environment is helping. Although it is time and labor intensive, our methodology provides researchers with a standard set of data and representations for comparing the programming processes promoted by alternative programming environments.

Abstract The field of software visualization (SV) investigates approaches and techniques for static and dynamic graphical representations of algorithms, programs (code), and processed data. SV is concerned primarily with the analysis of programs and their development. The goal is to improve our understanding of inherently invisible and intangible software, particularly when dealing with large information spaces that characterize domains like software maintenance, reverse engineering, and collaborative development. The main challenge is to find effective mappings from different software aspects to graphical representations using visual metaphors. This paper provides an overview of the SV research, describes current research directions, and includes an extensive list of recommended readings. 1