Do algorithm animations assist learning?: an empirical study and analysis
CHI '93 Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems
Empirical studies of the value of algorithm animation in algorithm understanding
Empirical studies of the value of algorithm animation in algorithm understanding
JHAVÉ—an environment to actively engage students in Web-based algorithm visualizations
Proceedings of the thirty-first SIGCSE technical symposium on Computer science education
Exploring the role of visualization and engagement in computer science education
Working group reports from ITiCSE on Innovation and technology in computer science education
Reification of Program Points for Visual Execution
VISSOFT '02 Proceedings of the 1st International Workshop on Visualizing Software for Understanding and Analysis
Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education
Taxonomy of effortless creation of algorithm visualizations
Proceedings of the first international workshop on Computing education research
JHAVÉ: Supporting Algorithm Visualization
IEEE Computer Graphics and Applications
Novel algorithm explanation techniques for improving algorithm teaching
SoftVis '06 Proceedings of the 2006 ACM symposium on Software visualization
WinHIPE: an IDE for functional programming based on rewriting and visualization
ACM SIGPLAN Notices
Getting algorithm visualizations into the classroom
Proceedings of the 42nd ACM technical symposium on Computer science education
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Visualization is a promising approach in improving the teaching of algorithms because it can give a pictorial representation of the effect of every step of an algorithm. However, traditional implementations of visualizations require much additional coding to support the infrastructure necessary to step through an algorithm. In this work, we embark on a different path for implementing visualizations, PCIL (PseudoCode Interpreted Language). We believe that PCIL distinguishes itself from other approaches to algorithm visualization by incorporating visualization into its specification. Each language primitive, such as a variable, natively supports a graphical representation. The PCIL interpreter automatically derives visualizations from algorithm implementations. In addition, PCIL includes constructs to facilitate pedagogically effective visualizations, such as the ability to specify custom inputs to algorithms and the ability to ask the student to predict algorithmic behavior. Experimental results indicate that not only do students enjoy using PCIL, they also perform much better on tests after using it compared to students who simply use traditional study aides. Furthermore, the students who use the application for longer amounts of time derive more benefit from the tool than those who only use it for a short time.