Judith D. Wilson

The benefits of collaboration for student programmers

Au experiment is described which supports the proposition that collaborative work can benefit problem-solving performance required in progr amming tasks typical of first year university level computing courses. The experimental results suggest that it may be worthwhile to find ways to integrate collaborative activities early into the computing curriculum.

An overview of visualization: its use and design: report of the working group in visualization

This paper presents an overview of visualization in Computer Science instruction. It is broken down in the following fashion. First, we present the motivation for using visualization and visual techniques in instruction. This is followed by a discussion of when the use of visualization is most appropriate. We then consider a broad spectrum of uses of visualization in Computer Science instruction. This spectrum is organized from passive to active in terms of a student’s involvement with the visualization tools. Types of visualizations are then categorized. The remainder of the paper focuses more on design issues for instructional visualization tools. These design issues are first presented from the perspective of the instructor who is constructing the visualization tool for students and then from the perspective of the programmer who is creating visualization software. We close the paper with some suggestions on organizing and maintaining a Web-based repository of visualization tools for Computer Science instruction.

Review of animation systems for algorithm understanding

We survey how several algorithm animation systorns are used in Computer Science instruction. Reported student reactions to the use of these systems is favorable, but little information is available on their effectiveness for learning. We examine several formal studies that have implications for how animation systems can most effectively be used to teach algorithms.

A repository that supports teaching and cooperation in the introductory AI course

This paper describes the development of FLAIR (Flexible Learning with an Artificial Intelligence Repository), a repository of educational material and of a highly visual computing environment for use in laboratories associated with the introductory undergraduate Artificial Intelligence (AI) course. This repository supports sharing of pedagogic material and of development tools, and cooperation in their use, while allowing diversity in content and in use at different institutions. Thus the development of the repository has stressed the production of system tools, extensible object-oriented libraries, and strong programming frameworks. Some modules currently available are on Search and Automated Reasoning. Examples of the presentation techniques used are conceptual maps, hypertext, and graphic animations of algorithms. Initial experience in the use of the repository in teaching the introductory AI course is taking place in the 1993/94 academic year.

Students' use of animations for algorithm understanding

Our goal in this pilot study is to explore students’ behavior as they learn about two search algorithms, observing the role of algorithm animations. We find that alternative animations of the same algorithm may provide different information and facilitate different types of reasoning.

Computer education in the primary and middle schools in the people's republic of china: The late 1980's

In 1987, a delegation of computer professionals with interests in computer science education participated in an information exchange with colleagues in the Peoples Republic of China. We report on the delegations observations of computer education in Chinese primary and middle schools. Our site visits and discussions with Chinese educators suggest that the Chinese have made substantial progress with introducing computing into the primary and middle schools since late 1982, but that difficult problems remain to be solved.

The introductory undergraduate AI course as observed on WWW

Most people who have taught the introductory undergraduate course in Artificial Intelligence agree that it is a difficult course to teach well. Recently attention has been directed to achieving a better understanding of the objectives, prerequisites, themes, pedagogic methodologies, topics, and support resources for this course [1]. Also, recently a number of professors who teach the course, acting independently have created and made available on the World Wide Web (WWW) simple Course Support Environments (CSEs) which include both pedagogic and administrative information.

Nonprogramming laboratory assignments for the introductory AI course using the FLAIR system

Undergraduate computer science students benefit from nonprogramming laboratory assignments which have them explore and experiment with algorithms and abstractions in a concrete learning environment. FLAIR, a repository of learning materials which supports the laboratory component of the introductory undergraduate AI course affords interesting nonprogramming assignments of this kind. In the following discussion the FLAIR system is described and three laboratory assignments which have been used with FLAIR, are presented.