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While the demand for college graduates with computing skills continues to rise, such skills no longer equate to mere programming skills. Modern day computing jobs demand design, communication, and collaborative work skills as well. Since traditional instructional methods in computing education tend to focus on programming skills, we believe that a fundamental rethinking of computing education is in order. We are exploring a new "studio-based" pedagogy that actively engages undergraduate students in collaborative, design-oriented learning. Adapted from architectural education, the studio-based instructional model emphasizes learning activities in which students (a) construct personalized solutions to assigned computing problems, and (b) present solutions to their instructors and peers for feedback and discussion within the context of "design crits." We describe and motivate the studio-based approach, review previous efforts to apply it to computer science education, and propose an agenda for multi-institutional research into the design and impact of studio-based instructional models. We invite educators to participate in a community of research and practice to advance studio-based learning in computing education.

Integrated Development Environments (IDE) generate multiple graphical and textual representations of programs. Co-ordination of these representations during program comprehension and debugging can be a complex task. In order to better understand the role and effectiveness of multiple representations, we conducted an empirical study of Java program debugging with a professional, multi-representation IDE. We found that program code and dynamic representations (dynamic viewer, variable watch and output) attracted the most attention of programmers. Static representations like Unified Modeling Language (UML) Diagrams and Control Structure Diagrams (CSD) saw significantly lesser usage. We analyzed gaze patterns by segmenting the debugging sessions into three, five and fifteen minute intervals, and classifying gazes into short and long gazes. Novel data mining techniques were used to detect high frequency patterns from eye tracking data. Visual pattern differences were found among participants based on their programming experience, familiarity with the IDE and debugging performance.