Jonathan Lee Brown

Can direct manipulation lower the barriers to computer programming and promote transfer of training?: An experimental study

Novices face many barriers when learning to program a computer, including the need to learn both a new syntax and a model of computation. By constraining syntax and providing concrete visual representations on which to operate, direct manipulation programming environments can potentially lower these barriers. However, what if the ultimate learning goal of the novice is to be able to program in conventional textual languages, as is the case for introductory computer science students? Can direct manipulation programming environments lower the initial barriers to programming, and, at the same time, facilitate positive transfer to textual programming? To address this question, we designed a new direct manipulation programming interface for novices, and conducted an experimental study to compare the programming processes and outcomes promoted by the direct manipulation interface against those promoted by a textual programming interface. We found that the direct manipulation interface promoted significantly better initial programming outcomes, positive transfer to the textual interface, and significant differences in programming processes. Our results show that direct manipulation interfaces can provide novices with a “way in” to traditional textual programming.

A methodology for analyzing the temporal evolution of novice programs based on semantic components

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! [17], 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 a model code solutions key semantic components, our methodology enables researchers 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.

The Effects of Algorithm Visualizations with Storylines on Retention: An Experimental Study

Algorithm visualizations graphically illustrate how algorithms work. In prior ethnographic studies of a computer science course in which students were required to construct and present their own algorithm visualizations, we observed that visualizations based on storylines tended to stimulate increased audience interest and involvement. This observation, coupled with the empirical research that substantiates the value of stories as mnemonic devices, raises an interesting research question: Do visualizations with storylines actually help students remember the procedural behavior of an algorithm better than visualizations that do not involve storylines? To investigate this question, we conducted an experimental study that compared the memorability of algorithm descriptions involving differing degrees of spatial and verbal embellishment. The study failed to detect significant differences. We reflect on our lack of significant results, and suggest two alternative paths for future research into the value of story-based algorithm visualization

Investigating collaborative self-modeling and its impact on introductory programming self-efficacy

This project aims to develop the collaborative self-modeling method for computer programming education, and to empirically evaluate its effectiveness. Based on the self-modeling method, the approach is designed to leverage simulated personal experiences to boost programming self-efficacy and related outcome variables such as course performance and persistence.

Code Collage: A Visual Meta-Language for Knowledge Structure Discovery

The programming assignments used in introductory computer science courses are insufficient as assessment tools. The project described here explores a visual programming meta-language, called code collage, as a medium for fuller expression, self-assessment, and understanding of novice programmer learning