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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Wang, April Yi Wu, Zihan Brooks, Christopher Oney, Steve Univ Michigan Sch Informat Ann Arbor MI 48109 USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450367080

When teams of data scientists collaborate on computational notebooks, their discussions often contain valuable insight into their design decisions. These discussions not only explain analysis in the current notebook but also alternative paths, which are often poorly documented. However, these discussions are disconnected from the notebooks for which they could provide valuable context. We propose Callisto, an extension to computational notebooks that captures and stores contextual links between discussion messages and notebook elements with minimal effort from users. Callisto allows notebook readers to better understand the current notebook content and the overall problem-solving process that led to it, by making it possible to browse the discussions and code history relevant to any part of the notebook. This is particularly helpful for onboarding new notebook collaborators to avoid misinterpretations and duplicated work, as we found in a two-stage evaluation with 32 data science students.

关键词： computational notebooks Collaborative Systems Data Science Literate Programming

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Case Study Comparison of computational Notebook Platforms for Interactive Visual Analytics

Case Study Comparison of Computational Notebook Platforms fo...

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IEEE Conference on Visualization in Data Science (VDS)

作者： Liu, Han North, Chris Virginia Tech Sanghani Ctr Blacksburg VA 24061 USA

ISBN: (数字)9781665457217

ISBN: (纸本)9781665457217

The way of using computational notebooks is quite different between data science and visual analytics. Data scientists focus on data exploration with the code, while visual analytics users are interested in engaging with interactive visual interfaces to facilitate analytical reasoning. Such a difference leads to design contradictions while merging visual analytics tools with data science tools in computational notebooks. In this work, we investigated the problem using an example called "Andromeda," which is an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python code in a Jupyter Notebook, 2) JavaScript code in an Observable Notebook, and 3) embedding both Python (data science use) and JavaScript (visual analytics use) in a Jupyter Notebook. Advantages and disadvantages are concluded for each platform by making comparisons based on various aspects, such as design logic, coding differences, performance, and usability. Laying the groundwork for data scientists, advice and recommendations are made on architecting similar notebooks and which platform to choose in various situations.

关键词： Visual Analytics Data Science computational notebooks Dimension Reduction

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Demonstration of ElasticNotebook: Migrating Live computational Notebook States 24

Demonstration of ElasticNotebook: Migrating Live Computation...

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ACM International Conference on Management of Data (SIGMOD)

作者： Li, Zhaoheng Chockchowwat, Supawit Fang, Hanxi Sahu, Ribhav Thakurdesai, Sumay Pridaphatrakun, Kantanat Park, Yongjoo Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798400704222

computational notebooks (e.g., Jupyter, Google Colab) are widely used for interactive data science and machine learning. However, existing notebook systems lack the functionality of reliably and efficiently persisting the notebook session state consisting of userdefined variables (e.g., processed datasets, ML models), hence the termination of a session often leads to loss of work. In this demo, we introduce a new notebook system, ElasticNotebook, that offers live migration of session states via computational checkpointing/restoration for notebook systems (e.g., Jupyter Notebook, Colab). ElasticNotebook's frontend allows users to configure the periodic creation of session state checkpoints, which can then be restored at will through a drop-down menu. ElasticNotebook's backend utilizes novel lightweight monitoring techniques to find a reliable and efficient way (i.e., replication plan) for replicating session states when requested. This demo will showcase ElasticNotebook's ability to preserve the user's work progress in Jupyter Servers by replicating their session state in two common use cases: live migration across machines and resumption after termination.

关键词： computational notebooks State Replication

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Albireo: An Interactive Tool for Visually Summarizing computational Notebook Structure

Albireo: An Interactive Tool for Visually Summarizing Comput...

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IEEE Conference on Visualization in Data Science (VDS)

作者： Wenskovitch, John Zhao, Jian Carter, Scott Cooper, Matthew North, Chris Virginia Tech Blacksburg VA 24061 USA FX Palo Alto Lab Palo Alto CA 94304 USA

ISBN: (纸本)9781728150475

computational notebooks have become a major medium for data exploration and insight communication in data science. Although expressive. dynamic. and flexible, in practice they are loose collections of scripts, charts, and tables that rarely tell a story or clearly represent the analysis process. This leads to a number of usability issues, particularly in the comprehension and exploration of notebooks. In this work, we design, implement. and evaluate Albireo, a visualization approach to summarize the structure of notebooks, with the goal of supporting more effective exploration and communication by displaying the dependencies and relationships between the cells of a notebook using a dynamic graph structure. We evaluate the system via a case study and expert interviews, with our results indicating that such a visualization is useful for an analyst's self-reflection during exploratory programming, and also effective for communication of narratives and collaboration between analysts.

关键词： Software visualization computational notebooks provenance analysis insight communication

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computational Notebook as a Tool for Supporting Black Engineers Non-Profit Organizations in the Context of Workforce Participation

Computational Notebook as a Tool for Supporting Black Engine...

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ACM CHI Conference on Human Factors in Computing Sytems (CHI)

作者： de Santana, Vagner Figueredo Quigley, Lauren Thomas Coates, Jessica Graves, Maxine IBM Res Yorktown Hts NY 10598 USA IBM Client Engn San Francisco CA USA

ISBN: (纸本)9798400703317

Data is frequently referred to as the 'new oil' due to its high strategic value and the possibilities it brings. Additionally, its use by powerful organizations has the potential to help, but also to cause harm and function as an additional oppressive mechanism towards vulnerable communities. Hence, there is a need for providing easier methods for people and organizations to access, analyze, and interpret reliable, publicly available data (e.g., U.S. Census), and create data narratives that reflect their social position in the society and inform how to improve that position. In this case study, we document our journey on using computational notebooks to increase autonomy for organizations tackling racial and social justice issues by making meaningful use of publicly available data. The project considered a participatory action research approach run in partnership with a non-profit organization supporting Black engineers. The reported case study involved participants representing staff and volunteer leadership of the organization. We expect that the presented reflections help move towards a more equitable way of performing data analysis while using popular and freely available technologies.

关键词： Participatory Action Research computational notebooks Jupyter Notebook Data Action Social Justice Racial Justice

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The computational Notebook Paradigm for Multi-Paradigm Modeling 22

The Computational Notebook Paradigm for Multi-Paradigm Model...

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ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

作者： Oakes, Bentley James Franceschini, Romain Van Mierlo, Simon Vangheluwe, Hans Univ Antwerp Antwerp Belgium Flanders Make Vzw Lommel Belgium Univ Cors Corte France

ISBN: (纸本)9781728151250

computational notebooks are gaining widespread acceptance as a paradigm for storage, dissemination, and reproduction of experimental results. In this paper, we define the computational notebook paradigm (CNP) consisting of entities and processes and discuss how the reproducibility of the experimental process and results is enhanced by each element. This paper also details the interactions of CNP and multi-paradigm modeling (MPM), with an aim of understanding how to support MPM within the CNP, and improve the reproducibility aspects of both the CNP and MPM.

关键词： computational notebooks Multi-paradigm modeling Paradigms Reproducibility Experimental Process

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Undergraduate and graduate students' conceptual understanding of model classification outcomes under the lens of scientific argumentation

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2024年第4期32卷 e22734-e22734页

作者： Wiese, Lucas Pinto, Hector E. Will Magana, Alejandra J. Purdue Univ Dept Comp & Informat Technol W Lafayette IN 47907 USA Univ Evansville Dept Elect Engn Evansville IN USA

Recent advancements in artificial intelligence (AI) and machine learning (ML) have driven research and development across multiple industries to meet national economic and technological demands. Consequently, companies are investing in AI, ML, and data analytics workforce development efforts to digitalize operations and enhance global competitiveness. As such, evidence-based educational research around ML is essential to provide a foundation for the future workforce as they face complex AI challenges. This study explored students' conceptual ML understanding through a scientific argumentation framework, where we examined how they used evidence and reasoning to support claims about their ML models. This framework lets us gain insight into students' conceptualizations and helped scaffold student learning via a cognitive apprenticeship model. Thirty students in a mechanical engineering classroom at Purdue University experimented with neural network ML models within a computational notebook to create visual claims (ML models) with textual explanations of their evidence and reasoning. Accordingly, we qualitatively analyzed their learning artifacts to examine their underfit, fit, and overfit models and explanations. It was found that some students tended toward technical explanations while others used visual explanations. Students with technically dominant explanations had higher proficiency in generating correctly fit models but lacked explanatory evidence. Conversely, students with visually dominant explanations provided evidence but lacked technical reasoning and were less accurate in identifying fit models. We discuss implications for both groups of students and offer future research directions to examine how positive pedagogical elements of learning design can optimize ML educational material and AI workforce development.

关键词： artificial intelligence computational notebooks evidence-based educational research mechanical engineering scientific argumentation workforce development

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Design Concerns for Integrated Scripting and Interactive Visualization in Notebook Environments

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第9期30卷 6572-6585页

作者： Scully-Allison, Connor Lumsden, Ian Williams, Katy Bartels, Jesse Taufer, Michela Brink, Stephanie Bhatele, Abhinav Pearce, Olga Isaacs, Katherine E. Univ Utah SCI Inst Salt Lake City UT 84112 USA Univ Tennessee Knoxville TN 37996 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Davidson Coll Math & Comp Sci Davidson NC 28035 USA Rincon Res Tucson AZ 85711 USA Larence Livermore Natl Lab Livermore CA 94550 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland Parallel Software & Syst Grp College Pk MD 20742 USA

Interactive visualization can support fluid exploration but is often limited to predetermined tasks. Scripting can support a vast range of queries but may be more cumbersome for free-form exploration. Embedding interactive visualization in scripting environments, such as computational notebooks, provides an opportunity to leverage the strengths of both direct manipulation and scripting. We investigate interactive visualization design methodology, choices, and strategies under this paradigm through a design study of calling context trees used in performance analysis, a field which exemplifies typical exploratory data analysis workflows with Big Data and hard to define problems. We first produce a formal task analysis assigning tasks to graphical or scripting contexts based on their specificity, frequency, and suitability. We then design a notebook-embedded interactive visualization and validate it with intended users. In a follow-up study, we present participants with multiple graphical and scripting interaction modes to elicit feedback about notebook-embedded visualization design, finding consensus in support of the interaction model. We report and reflect on observations regarding the process and design implications for combining visualization and scripting in notebooks.

关键词： computational notebooks exploratory data analysis hybrid visualization-scripting interactive data analysis visualization design computational notebooks exploratory data analysis hybrid visualization-scripting interactive data analysis visualization design

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Toward Code Review notebooks 22

Toward Code Review Notebooks

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22nd IEEE International Conference on Advanced Learning Technologies (ICALT)

作者： Farah, Juan Carlos Spaenlehauer, Basile Rodriguez-Triana, Maria Jesus Ingram, Sandy Gillet, Denis Ecole Polytech Fed Lausanne EPFL Sch Engn Lausanne Switzerland Tallinn Univ Sch Digital Technol Tallinn Estonia Univ Appl Sci HES SO Sch Engn & Architecture Fribourg Fribourg Switzerland

ISBN: (纸本)9781665495196

Peer code review has proven to be a valuable tool in software engineering. However, integrating code reviews into educational contexts is particularly challenging due to the complexity of both the process and popular code review tools. We propose to address this challenge by designing a code review application (CRA) aimed at teaching the code review process directly within existing online learning platforms. Using the CRA, instructors can scaffold online lessons that introduce the code review process to students through code snippets, following a format resembling computational notebooks. We refer to this online lesson format as the code review notebook format. Through a case study comprising an online lesson on code quality standards completed by 23 university students, we evaluated the usability of the CRA and the code review notebook format, obtaining positive results for both. These results are a first step toward integrating code review notebooks into software engineering education.

关键词： code review software engineering education code quality online learning computational notebooks

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How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study 24

How Do Data Analysts Respond to AI Assistance? A Wizard-of-O...

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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Gu, Ken Grunde-McLaughlin, Madeleine McNutt, Andrew Heer, Jefrey Althof, Tim Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798400703300

Data analysis is challenging as analysts must navigate nuanced decisions that may yield divergent conclusions. AI assistants have the potential to support analysts in planning their analyses, enabling more robust decision making. Though AI-based assistants that target code execution (e.g., Github Copilot) have received significant attention, limited research addresses assistance for both analysis execution and planning. In this work, we characterize helpful planning suggestions and their impacts on analysts workflows. We first review the analysis planning literature and crowd-sourced analysis studies to categorize suggestion content. We then conduct a Wizard-of-Oz study (n=13) to observe analysts preferences and reactions to planning assistance in a realistic scenario. Our findings highlight subtleties in contextual factors that impact suggestion helpfulness, emphasizing design implications for supporting different abstractions of assistance, forms of initiative, increased engagement, and alignment of goals between analysts and assistants.

关键词： Data Analysis Statistical Analysis computational notebooks Artificial Intelligence Code Assistant Copilot Wizard of Oz HumanAI Interaction Human-LLM Interaction Human-AI Collaboration Data Science Assistant Analysis Tools Analysis Planning Human-Centered Data Science

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