Computer science curricula has been well defined for many years through the publication of the Computer Science Curricula reports developed jointly by the two major professional societies, the Association for Computing Machinery (ACM) and the IEEE Computer Society. These documents define computer science curricula by providing knowledge areas and course exemplars. The most recent curriculum report, the Computer Science Curricula 2013 (CSC13 [1]), provides 18 knowledge areas (KAs). Though it stresses that KAs do not necessary represent courses, computer science departments have traditionally created courses around the KAs. Indeed, the course exemplars presented in the CSC13 report, for the most part, center around KAs.

This paper reports on the results of a multi-year project in which we identified essential pedagogy and curriculum for teaching introductory computing courses focused on Creative Computation using Processing. The curriculum aligns with a traditional 'CS1' approach as well as 'AP CS A', and goes well beyond "CS Principles" standards to teach foundations of computer science and programming. We addressed the bridge between high school and entry-level college curriculum in computer science (American freshman high school to freshman college) and demonstrated how algorithmic art provides a powerful vehicle for diverse student populations within a broad range of pedagogical frameworks ranging from traditional structured classrooms to inquiry-based student-driven project labs. A secondary result is that instructors require long-term engagement with mentors to extend their own knowledge of computing, visual arts and appropriate pedagogy.

No abstract available.

In this paper we describe the success of bringing Creative Computation via Processing into two very different high schools that span the range of possibilities of grades 9-12 in American education. Creative Computation is an emerging discipline that requires a thorough grounding in both media arts and computing. We report on how contextualized computing that supports integration of media arts, design, and computer science can successfully attract and motivate students to learn foundations of programming and come back for more. The work of two high school teachers with divergent pedagogical styles is presented. They successfully adapted a college-level Creative Computation curriculum to their individual school cultures providing a catalyst for significant increases in total enrollment as well as female participation in high school computer science.

This workshop showcases an engaging way to attract students who typically avoid a traditional introductory Computer Science course (CS1), with fully developed, classroom-tested course materials. This workshop has been successful at SIGCSE and other venues in the past. This year we highlight our successful approach in pre-AP courses, as well as continued refinement of curriculum for college-level CS1. Our courses focus on essential CS1 principles, but show applications of these principles with contemporary, diverse examples of computing in a modern context, including advanced areas typically not accessible in CS1 such as: physics-based simulations, fractals and L-systems, image processing, emergent systems, cellular automata and data visualization. Students produce dynamic visual work using the programming language Processing, which is fully compatible with Java. We aim to inspire the Computer Science community to use innovative and creative approaches to attract a broader audience to their classes.

This workshop showcases a new approach to teaching CS1 using computational art as a context. Participants will be introduced to the Processing programming language and environment, designed for the construction of 2D and 3D visual forms. Its IDE is light-weight, but well-suited for the rapid proto-typing needed for dynamic visual work. We hope to bring the excitement, creativity, and innovation fostered by Processing into the computer science education community. Instructors of all experience levels are welcome. Hands-on portion of the workshop will enable participants to explore Processing and create visual effects on the fly. Course materials and handouts detailing the software, curriculum, and teaching resources will be given out. All participants will need to bring their own laptops. Supported by NSF Awards DUE-0942626 and CCF-0939370.

In this paper, we present the design and development of a new approach to teaching the college-level introductory computing course (CS1) using the context of art and creative coding. Over the course of a semester, students create a portfolio of aesthetic visual designs that employ basic computing structures typically taught in traditional CS1 courses using the Processing programming language. The goal of this approach is to bring the excitement, creativity, and innovation fostered by the context of creative coding. We also present results from a comparative study involving two offerings of the new course at two different institutions. Additionally, we compare our results with another successful approach that uses personal robots to teach CS1.

This workshop showcases a new approach to teaching CS1 using computational art as a context. Participants will be introduced to the Processing programming language and environment, designed for the construction of 2D and 3D visual forms. Its IDE is lightweight, but well-suited for the rapid prototyping needed for dynamic visual work. We hope to bring the excitement, creativity, and innovation fostered by Processing into the computer science education community. Instructors of all experience levels are welcome. Hands-on portion of the workshop will enable participants to explore Processing and create visual effects on the fly. Course materials and handouts detailing the software, curriculum, and teaching resources will be given out. All participants will need to bring their own laptops.

The SIGCSE community has been exploring the role of multimedia to enhance computing education since the earliest algorithm visualization systems and studies [1]. Media Computation is a shift in focus [2]. Where algorithm visualization presents information to the student to facilitate their understanding, media computation is about having students manipulate media as the data for their programming, i.e., as the focus of the course activities. Students in media computation produce new images, sounds, and video. We aim to show that computer science is about more than numbers and strings. Computer science is also about creative expression. The original media computation work focused on using media to motivate non-computing majors [2]. The role of media in motivating student learning for computing education has broadened. Inventive teachers are using media computation for lots of different kinds of students, at different kinds of institutions, with a range of languages and toolkits. This special session is a mixture of "Five Minute Madness," science fair, and art gallery. Each participant will present how he or she is using media to motivate student learning, and some student work will be available for audience inspection

We describe myro.chuck, a Python module for controlling music synthesis, and its applications to teaching introductory computer science. The module was built within the Myro framework using the ChucK programming language, and was used in an introductory computer science course combining robots, graphics and music. The results supported the value of music in engaging students and broadening their view of computer science.

We have developed a CS1 curriculum that uses a robotics context to teach introductory programming [1]. Core to our approach is that each student has their own personal robot. Our robot and software have been specifically developed to support the needs of a CS1 curriculum. We frame traditional problems (robot control) in terms that are personal, relevant, and fun. Initial trial classes have shown that our approach is successful and adaptable.

No abstract available.

We propose an intrinsic developmental algorithm that is designed to allow a mobile robot to incrementally progress through levels of increasingly sophisticated behavior. We believe that the core ingredients for such a developmental algorithm are abstractions, anticipations, and self-motivations. We describe a multilevel, cascaded discovery and control architecture that includes these core ingredients. As a first step toward implementing the proposed architecture, we explore two novel mechanisms: a governor for automatically regulating the training of a neural network and a path-planning neural network driven by patterns of “mental states” that represent protogoals.

No abstract available.

In this article we describe a programming framework called Pyro, which provides a set of abstractions that allows students to write platform-independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top-down approach. We describe the background of the project, its novel abstractions, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students' perspective in a case study.

No abstract available.

No abstract available.

Male and female students often hold different views of the culture within the same computer science department. These differences may, in part, account for why women are underrepresented in computer science. We found that surveying students about their views of our departments' environments was an important first step in evaluating the cultures of our own departments, in determining what issues needed to be addressed, and in determining how to address them. Our survey results revealed some problems in our classroom and lab environments, and showed that there are gender differences in students' perceptions of our departments. We describe a set of changes that were implemented in response to our findings. These solutions are specifically designed to address problems that we discovered through our student survey, but they are not all original to us. The contribution of our work is in demonstrating how surveying is critical to identifying and understanding problems in our departments. We argue that a process of continually surveying students is vital to the maintenance and evolution of a healthy computer science program.

This paper describes Pyro, a robotics programming environment designed to allow inexperienced undergraduates to explore topics in advanced robotics. Pyro, which stands for Python Robotics, runs on a number of advanced robotics platforms. In addition, programs in Pyro can abstract away low-level details such that individual programs can work unchanged across very different robotics hardware. Results of using Pyro in an undergraduate course are discussed.

In this article we describe a programming framework called Pyro, which provides a set of abstractions that allows students to write platform-independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top-down approach. We describe the background of the project, its novel abstractions, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students' perspective in a case study.

We present a "state of the course" report on a core computer science topic: Computer Organization & Architecture. Our report is based on a survey of over 80 faculty, at colleges and universities around the world, who teach this topic. Based on the responses in the survey, we present our conclusions on the confidence levels of faculty in teaching various core components of the topic. We will present our results in the context of the identified core body of knowledge as defined in the Curriculum 2001. These conclusions can be of help to experts who are willing to provide training and/or pedagogical materials in order to assist those with low confidence levels.

There is a growing consensus among computer science faculty that it is quite difficult to teach the introductory course on Artificial Intelligence well [4, 6]. In part this is because AI lacks a unified methodology, overlaps with many other disciplines, involves a wide range of skills from very applied to quite formal. In the funded project described here we have addressed these problems Offering a unifying theme that draws together the disparate topics of AI; Focusing the course syllabus on the role AI plays in the core computer science curriculum; and Motivating the students to learn by using concrete, hands-on laboratory exercises.Our approach is to conceive of topics in AI as robotics tasks. In the laboratory, students build their own robots program them to accomplish the tasks. By constructing a physical entity in conjunction with the code to control it, students have a unique opportunity to directly tackle many central issues of computer science including the interaction between hardware software, space complexity in terms of the memory limitations of the robot's controller, time complexity in terms of the speed of the robot's action decisions. More importantly, the robot theme provides a strong incentive towards learning because students want to see their inventions succeed.This robot-centered approach is an extension of the agent-centered approach adopted by Russell Norvig in their recent text book [11]. Taking the agent perspective, the problem of AI is seen as describing building agents that receive perceptions as input then output appropriate actions based on them. As a result the study of AI centers around how best to implement this mapping from perceptions to actions. The robot perspective takes this approach one step further; rather than studying software agents in a simulated environment, we embed physical agents in the real world. This adds a dimension of complexity as well as excitement to the AI course. The complexity has to do with additional demands of learning robot building techniques but can be overcome by the introduction of kits that are easy to assemble. Additionally, they are lightweight, inexpensive to maintain, programmable through the standard interfaces provided on most computers, yet, offer sufficient extensibility to create experiment with a wide range of agent behaviors. At the same time, using robots also leads the students to an important conclusion about scalability: the real world is very different from a simulated world, which has been a long standing criticism of many well-known AI techniques.We proposed a plan to develop identical robot building laboratories at both Bryn Mawr Swarthmore Colleges that would allow us to integrate the construction of robots into our introductory AI courses. Furthermore, we hoped that these laboratories would encourage our undergraduate students to pursue honors theses research projects dealing with the building of physical agents.