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Interactive Programming Interfaces for Data Science Collaboration and Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Interactive Programming Interfaces for Data Science Collaboration and Learning./
Author:	Yi Wang, April.
Description:	1 online resource (293 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Information science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380372695

Interactive Programming Interfaces for Data Science Collaboration and Learning.
Yi Wang, April.

Interactive Programming Interfaces for Data Science Collaboration and Learning. - 1 online resource (293 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of Michigan, 2023.

Includes bibliographical references

With the expanding landscape of data science, it becomes increasingly crucial for data scientists to foster collaborative practices to enhance productivity. Computational notebooks enable data scientists to run and share exploratory analysis. The notebook design facilitates the rapid iteration of code chunks and inspection of intermediate results through an interactive execution interface. The combination of exploratory code and human-readable explanations creates computational narratives that are effortlessly shared and reproduced. This dissertation investigates the challenges associated with the ineffective usage of computational notebooks for collaboration, and proposes possible interactive designs to improve the notebooks for both data science professionals and educational settings. This dissertation unfolds into three parts aimed at enhancing the usability of data science programming environments. Firstly, to discern the obstacles faced in collaborative data science, I conducted mixed-methods inquiries to understand how data scientists currently utilize real-time collaborative editing in computational notebooks. Based on the findings, I proposed a series of design approaches for addressing the challenges, including providing contextually-linked discussions, leveraging AI-assisted data science code documentation, visualizing data changes, and lightweight editing control mechanisms that can make computational notebooks more narrative. Lastly, my dissertation illuminates the distinct collaboration needs required to support data science teaching and learning. I explored a series of designs that support live peer evaluation for testing and reviewing in-class programming exercises and authoring explorable multi-stage tutorials. In summary, my dissertation contributes to improving the usability and effectiveness of computational notebooks for data science collaboration and learning, facilitating their ability to create and share computational narratives.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380372695Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Computational notebooksIndex Terms--Genre/Form:

554714
Electronic books.

Interactive Programming Interfaces for Data Science Collaboration and Learning.
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