Genevieve Orr

Developing a community definition and teaching modules for computational thinking: accomplishments and challenges

The NSF-funded Northwest Distributed Computer Science Department (NW-DCSD) project brings together 24 multi-disciplinary faculty from 19 diverse colleges and universities in an effort to change the face of computing in the Pacific Northwest region of the United States. We offer an innovative and inclusive vision of computing in the 21st century and foster opportunities for multi-disciplinary and inter-institutional computing and computer science education collaborations. Over the projects first two years, this community has created 9 engaging, easy-to-use learning modules that teach various levels of computational thinking to two different audiences: non-computer science and computer science undergraduate students. This paper presents the development of a community definition of computational thinking, the learning modules, initial findings, unanticipated challenges, and next steps.

knowledge base for the emerging discipline of computer graphics

Computer Graphics is evolving as a discipline characterized by the fusion of artistic and technical theories and skills. The goal of the SIGGRAPH Curriculum Working Group has been to create a knowledge base that defines this discipline. This knowledge base is presented as a palette of subject areas and skills that forms the necessary educational framework for creation of undergraduate curricula that specialize in computer graphics. It facilitates the development of attributes that will create paths toward professional work, graduate studies, and lifelong skills-development and learning. The knowledge base is offered for faculty members designing new computer graphics programs or those evolving existing ones, students crafting their own programs of study, and administrators and accreditors seeking guidance for framing and assessing computer graphics programs.

Computational thinking through programming and algorithmic art

General education students can be taught computational thinking skills through courses that marry computer programming with algorithmic art. Algorithmic art is a varied and growing field where images are generated on the computer using mathematical and computer algorithms (see http://processing.org/exhibition/index.html for examples). Visually oriented students may be motivated to learn programming when it is taught in a context that is as much focused on art, artists, and design principles as it is on mathematics and programming. This presentation will include 1) a discussion of the challenges of teaching programming, 2) the role of teaching style, motivation, and programming environment, 3) a summary of common algorithms and their relation to design principles, and 4) examples of artwork that has been created by current artists.

A new model of interaction: digital music and 3D animation

How do we get students to think less about the mechanics of multimedia software and more about the mediums creative potential for telling a story? One way is through collaboration with students in different but synergistic disciplines. This paper presents a new model for interdisciplinary collaborative teaching which provides a more flexible alternative to the traditional team-teaching approach. The model is applied to 3D computer animation and digital music.

Visualizing valley wind flow

The field of micrometeorology is primarily concerned with smaller-scale meteorological phenomena, specifically those which occur within the lowest atmospheric layer called the Atmospheric Boundary Layer (ABL). The primary defining characteristic of the ABL is that wind dynamics within this layer are influenced by the Earths topography, as well as time-dependent temperature changes in the Earths surface. In forests and connected valleys, weak-wind flows transport moisture, heat, gases and potential contaminants, directly impacting adjacent ecosystems [Thomas et al. 2012].

Representing and Incorporating Prior Knowledge in Neural Network Training: Preface

The present section focuses on tricks for four important aspects in learning: (1) incorporation of prior knowledge, (2) choice of representation for the learning task, (3) unequal class prior distributions, and finally (4) large network training.