Spring 2017¶

4 / 14 Prof. Chris Mayfield¶

Adopting CS Principles in a Breadth-First Survey Course

With the recent launch of AP CS Principles in 2016-17, many efforts are currently underway to share curriculum resources and prepare new teachers. The community has primarily focused on high school implementations, which have different situational factors than university courses (e.g., amount of class time). In this seminar, we present the design of a survey course that aligns with CS Principles and also continues the long tradition of breadth-first introductions to computer science at the college level. We describe the instructional strategies, assessments, and curriculum details, providing a model for how to modify existing CS0 courses. We also outline twelve lab activities that support the computational thinking practices and learning objectives of the AP curriculum framework. The course has run successfully for the past four years at two universities and three high schools via dual enrollment. Initial results suggest that the curriculum has a positive impact on student confidence levels and attitudes toward computer science.

3 / 31 Prof. Dee Weikle¶

Workload Characterization: Some Motivation and Some Math

Workload characterization, including establishing benchmarks, has become a critical part of computer architecture research. At its core, though, it is manipulating very large amounts of data. This talk will discuss some of the motivation behind doing workload characterization and at least one of the mathematical tools, Principal Component Analysis, that computer architects use to do characterization.

3 / 3 Prof. Nathan Sprague¶

Deep Neural Networks vs. Space Invaders: Teaching a Computer to Play Atari

Neural networks have seen a resurgence over the last decade as a result of algorithmic and hardware advances that have enabled the use of deep network architectures. These deep neural networks are now the dominant approach for a wide range of challenging computational problems. Some networks have shown human-level (or better) performance on tasks including visual object recognition, game playing, and translation. In this talk I will introduce artificial neural networks, describe recent progress in deep neural networks, and discuss the application of these networks to problems that involve reasoning and decision making.

2 / 10 Prof. Michael Kirkpatrick¶

Two Talks for the Price of One: Evaluating CS1 Changes and Cognitivism as a CS Ed Foundation

The first part of this talk presents the results of empirical research examining the impact of recent changes made to the introductory CS courses at JMU. We have examined 10 years of data to examine whether the JMU CS introductory sequence is achieving its goal of providing a basis for all students to succeed in the major, regardless of prior experience.

The second part of this talk discusses why more research on CS education is needed. After presenting some counterintuitive and surprising empirical results, we will introduce cognitivism as a theory of learning. This theory uses principles of human cognitive architecture to explain how learning occurs. Cognitive load theory (CLT) builds on these principles to create a basis for efficient and enduring acquisition of biologically secondary knowledge. We will close by discussing the possibility that CLT provides a good framework for effective CS teaching.

1 / 27 Prof. John C. Bowers¶

Circle packings and Polyhedra

In this talk I will survey some recent developments in the field of circle packing, a theory of circle patterns that are packed together so that neighboring circles are tangent. Circle packings are theoretically interesting as discretizations of analytic functions and provide a method of computing maps between spaces, called quasi-conformal maps, that (approximately) maintain angles. A variety of interesting applications make use of circle packings, including brain anatomy mapping, computational experiments for investigating certain random-walk behaviors in quantum mechanics, and graph drawing. I will introduce the field of circle packing, discuss several of our recent theoretical results, and discuss a new heuristic for computing circle packings that we are currently applying to 3D-printing related applications.