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Award Abstract #1314399

HCC: Large: Collaborative Research: Variations to Support Exploratory Programming

NSF Org: IIS
Div Of Information & Intelligent Systems
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Initial Amendment Date: August 7, 2013
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Latest Amendment Date: August 7, 2013
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Award Number: 1314399
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Award Instrument: Standard Grant
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Program Manager: Ephraim P. Glinert
IIS Div Of Information & Intelligent Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: August 1, 2013
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End Date: July 31, 2017 (Estimated)
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Awarded Amount to Date: $356,216.00
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Investigator(s): Andrew Ko ajko@uw.edu (Principal Investigator)
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Sponsor: University of Washington
4333 Brooklyn Ave NE
Seattle, WA 98195-0001 (206)543-4043
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NSF Program(s): Cyber-Human Systems (CHS)
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Program Reference Code(s): 7367, 7925
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Program Element Code(s): 7367

ABSTRACT

In any design or learning activity, exploration is a key component. Significant research and conventional wisdom show that the best way to achieve a high-quality design is to explore multiple variations and iteratively evaluate them. When novices learn a new skill or system, they must explore and practice the available options. Similarly, when experts try to understand and improve an existing design, they must explore different approaches to modifying its behavior. Unfortunately, exploration is risky, error-prone, and cumbersome using today's tools. For instance, when users decide their current design is not effective, the only mechanisms available for selectively backtracking out of changes are linear undo and version control, which make it difficult to isolate backtracking to specific edits, or else users must manually remove undesired edits, which is slow and fallible. Further, today's tools do not support comparing two variants of a design or combining elements from multiple variants. Research is showing that these manual processes inhibit exploration, making users and designs less effective.

To address these problems PIs from four partner institutions have come together to undertake a research program that is both broad and deep, focusing on the creation and management of variations during a system's implementation and evolution. The goal is to discover new theories, algorithms, visualizations, and tools that support variations in code. The team will evaluate all of their approaches through lab and field studies, and they will investigate how users can be educated in more effective ways to work with variations. Based on a choice calculus for representing variations in software, they will develop a theory for formally defining and reasoning about variations. They will leverage theories of human behavior such as Minimalist Learning, Attention Investment, and Information Foraging, to develop a theory of Variation Foraging. They will develop an infrastructure including multiple levels of transcripts of users' editing operations that will support a novel form of selective undo and enable users to investigate their existing variants, return to any previous variant, and mix and match elements from multiple variants. They will develop algorithms to enable recording of interactions with variants so they can be explored and reused to explore and test new variants; these recordings will be augmented with automatically created data to help users understand behaviors they have not explicitly explored. Using this infrastructure the PIs will invent visualizations, search facilities, and interaction techniques that provide effective ways for users to find, understand, explore, reuse and create variants, and be able to ask "why" questions to understand the differences among variations of a system. For novices, an "Idea Garden" will help them explore new strategies for identifying which variations can help solve a problem and how to implement them.

Broader Impacts: This research will enhance infrastructure for research and education by producing an integrated, open source web development environment for use by researchers and the world. The work will therefore benefit society by empowering the tens of millions of end-user programmers to creatively build content and applications for the web. The PIs will advance discovery while promoting learning by integrating their research into undergraduate courses on creativity and software engineering, and by supporting summer camps for at least 300 high school students per year. Project outcomes will be disseminated to researchers through publications and presentations, to computing educators through the above-mentioned camps and the National Girls Collaborative Project, and through public deployment. The PIs expect high interest because the work will be based on JavaScript, which is today's most popular programming language and for which there is a high demand for better tools. The research will address underrepresentation via its focus on investigating how to support both male and female end-user programmers, by involving high-school members of underrepresented groups, and by engaging many of the PIs? female students.

 

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