This research investigated how the similarity of the rendering parameters of background and foreground objects affected egocentric depth perception in indoor virtual and augmented environments. We refer to the similarity of the rendering parameters as visual 'congruence'. Study participants manipulated the depth of a sphere to match the depth of a designated target peg. In the first experiment, the sphere and peg were both virtual, while in the second experiment, the sphere is virtual and the peg is real. In both experiments, depth perception accuracy was found to depend on the levels of realism and congruence between the sphere, pegs, and background. In Experiment 1, realistic backgrounds lead to overestimation of depth, but resulted in underestimation when the background was virtual, and when depth cues were applied to the sphere and target peg. In Experiment 2, background and target pegs were real but matched with the virtual sphere; in comparison to Experiment 1, realistically rendered targets prompted an underestimation and more accuracy with the manipulated object. These findings suggest that congruence can affect distance estimation and the underestimation effect in the AR environment resulted from increased graphical fidelity of the foreground target and background.

This workshop will provide instructors in early CS courses with tools and strategies for designing and building high quality courses by structure and content, that are student centered, aligned with stated learning outcomes, and with access to engaging learning materials. Workshop participants will be introduced to two software toolkits, CS Materials and BRIDGES, towards achieving these goals. These tools permit searches for learning materials that meet specific learning outcomes, while at the same time provide access to engaging materials that demonstrate core CS relevance to real world problems and applications. Workshop participants will be exposed to strategies for designing courses, materials and tools that can engage today's students and meet their expectations. Although this workshop will be based on CS Materials and BRIDGES, the lessons learnt are independently beneficial to participants.

Computing proficiency is an increasingly vital component of the modern workforce, and computer science programs are faced with the challenges of engaging and retaining students to meet the growing need in that sector. However, administrators and instructors often find themselves either reinventing the wheel or relying too heavily on intuition, despite the availability of national curriculum standards. To address these issues, we present CS Materials, an open-source resource targeted at computing educators for designing and analyzing courses for coverage of recommended guidelines, and alignment between the various components within a course, between sections of the same course, or course sequences within a program. The system works by facilitating mapping educational materials to national curriculum standards. A side effect of the system is that it centralizes the design of the courses and the materials used therein. The curriculum guidelines act as a lingua franca that allows examination of and comparison between materials and courses. More relevant to instructors, the system enables a more precise search for materials that match particular topics and learning outcomes, and dissemination of high quality materials and course designs. This paper discusses the system, and analyzes the costs and benefits of its features and usage. While adding courses and materials requires some overhead, having a centralized repository of courses and materials with a shared structure and vocabulary serves students, instructors, and administrators, by promoting a data-driven approach to rigor and alignment with national standards.

Grounding computer science concepts in real-world and socially relevant problems can be a key to increasing students' motivation and engagement in computing. BRIDGES provides an infrastructure for use in early CS courses that allows students to easily integrate real-world data into their routine course assignments and visualize the data and data structures of their implementations. The BRIDGES API provides several key advantages, First, minimal effort is needed to accessing and using interesting real-world datasets in CS homework assignments. Available data sets in BRIDGES span multiple domains, including entertainment, science, geographic data, and literature. Second, BRIDGES can be used by students to create and explore a visualization of the data used and data structures implemented in their assignments. Such visualizations can be used to illustrate concepts of underlying algorithm or data structure, or important data features. Third, the BRIDGES Game API allows students to implement 2D games, to emphasize basic concepts, such as control structures, looping and logic, while providing a fun experience. Finally, students can explore the benchmarking of algorithms in BRIDGES by using real and large data sets, emphasizing algorithm performance and computational complexity. Workshop attendees will engage in hands-on experience with BRIDGES with multiple datasets and will have opportunities to discuss how BRIDGES can be used in their own courses.

Early programming courses such as CS1 are an important time to capture the interest of students while imparting critical technical knowledge. Yet many CS1 courses are being taught using toy assignments and activities that tend to make students uninterested or doubt the usefulness of the content. In this poster, we demonstrate an enriching experience for students by coupling interesting datasets with visual representations and interactive applications, without having to change the content of that course. Our approach utilizes extensions to BRIDGES, an API in use for sophomore level CS courses for the past 5 years. BRIDGES provides easy access to external datasets and helps build interactive applications. The assignments we present are all scaffolded in a way that can be directly integrated into most early programming courses to make routine topics compelling and exciting.

In recent years, interactive textbooks have gained prominence in an effort to overcome student reluctance to routinely read textbooks, complete assigned homeworks, and to better engage students to keep up with lecture content. Interactive textbooks are more structured, contain smaller amounts of textual material, and integrate media and assessment content. While these are an arguable improvement over traditional methods of teaching, issues of academic integrity and engagement remain. In this work we demonstrate preliminary work on building interactive teaching modules for data structures and algorithms courses with the following characteristics, (1) the modules are highly visual and interactive, (2) training and assessment are tightly integrated within the same module, with sufficient variability in the exercises to make it next to impossible to violate academic integrity, (3) a data logging and analytic system that provides instantaneous student feedback and assessment, and (4) an interactive visual analytic system for the instructor to see students/ performance at the individual, sub-group or class level, allowing timely intervention and support for selected students. Our modules are designed to work within the infrastructure of the OpenDSA system, which will promote rapid dissemination to an existing user base of CS educators. We demonstrate a prototype system using an example dataset.