Frank Hadlock

Pedagogically effective effortless algorithm visualization with a PCIL

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.

Temporal consistency checking of natural language specifications

Abstract not available.

A tabular parsing algorithm with error recovery

In this article, a general tabular parsing algorithm is developed which permits isolated errors due to deletion, replacement, or insertion of a word or phrase, and which finds the derivation with minimum errors.