retreats:2022fall:abstracts
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| - **Document Organization Three Ways**\\ Despite advances in natural language processing, computer vision, and other techniques that simplify the processing of large, unstructured documents such as PDFs, present-day tools remain difficult to use. Many experts from non-technical domains continue to process large, messy document datasets manually, while others become self-taught programmers. For teams with limited time, budgets, and computing education, this is a heavy burden. Our study assesses the learnability of three categories of programming interaction for document processing: textual, visual, and programming-by-example. We conducted a counterbalanced within-subject study (n=12) in which participants used all three programming paradigms. Our qualitative analysis reveals patterns in their relative benefits, including how participants reported Visual programming paradigms gave them a broader understanding of their data. Our results suggest design opportunities for tools that aim to help domain experts complete programming tasks.\\ \\ | - **Document Organization Three Ways**\\ Despite advances in natural language processing, computer vision, and other techniques that simplify the processing of large, unstructured documents such as PDFs, present-day tools remain difficult to use. Many experts from non-technical domains continue to process large, messy document datasets manually, while others become self-taught programmers. For teams with limited time, budgets, and computing education, this is a heavy burden. Our study assesses the learnability of three categories of programming interaction for document processing: textual, visual, and programming-by-example. We conducted a counterbalanced within-subject study (n=12) in which participants used all three programming paradigms. Our qualitative analysis reveals patterns in their relative benefits, including how participants reported Visual programming paradigms gave them a broader understanding of their data. Our results suggest design opportunities for tools that aim to help domain experts complete programming tasks.\\ \\ | ||
| - **Exploring the Learnability of Program Synthesizers by Novice Programmers**\\ Tools known as program synthesizers show promise to lighten the burden of programming by automatically writing code for users, but little research has addressed what contributes to and detracts from their learnability by novice programmers. For example: | - **Exploring the Learnability of Program Synthesizers by Novice Programmers**\\ Tools known as program synthesizers show promise to lighten the burden of programming by automatically writing code for users, but little research has addressed what contributes to and detracts from their learnability by novice programmers. For example: | ||
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| - | From our analysis, we provide a set of design opportunities to inform the design of future program synthesizers. Our findings have ramifications for the use of program synthesis in data work.\\ \\ | + | |
| - **Always-on Visualization Recommendations**\\ Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating visual insight discovery in dataframe workflows. When a dataframe is printed, Lux recommends visualizations to provide a quick overview of the patterns and trends and suggest promising analysis directions. Users can tailor recommendations via a lightweight intent language. Lux also leverages scalable data computation techniques to generate recommendations quickly. Lux has been embraced by data science practitioners -- and especially by novice data scientists -- with over 400K downloads and 4.2k stars on Github.\\ \\ | - **Always-on Visualization Recommendations**\\ Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating visual insight discovery in dataframe workflows. When a dataframe is printed, Lux recommends visualizations to provide a quick overview of the patterns and trends and suggest promising analysis directions. Users can tailor recommendations via a lightweight intent language. Lux also leverages scalable data computation techniques to generate recommendations quickly. Lux has been embraced by data science practitioners -- and especially by novice data scientists -- with over 400K downloads and 4.2k stars on Github.\\ \\ | ||
| - **Human-Centered Tools for Reliable Use of Machine Translation**\\ Although machine translation (MT) technology has been rapidly improving, actual user needs for these systems remain relatively poorly understood and, as a result, unmet. For example, current MT systems do not help users understand when they can rely on translations, | - **Human-Centered Tools for Reliable Use of Machine Translation**\\ Although machine translation (MT) technology has been rapidly improving, actual user needs for these systems remain relatively poorly understood and, as a result, unmet. For example, current MT systems do not help users understand when they can rely on translations, | ||
retreats/2022fall/abstracts.1667514519.txt.gz · Last modified: 2022/11/03 15:28 by kilov
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