Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-...
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Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-solving. In this paper, we aim to advance the understanding of learners' use of a support strategy (hints) when solving data science programming tasks in an online AI-enabled learning environment. The study compared two conditions: students solving problems in DaTu with AI assistance ( N = 45) and those without AI assistance ( N = 44). Findings reveal no difference in hint-seeking behavior between the two groups, suggesting that the integration of AI assistance has minimal impact on how individuals seek help. The findings also suggest that the availability of AI assistance does not necessarily reduce learners’ reliance on support strategies (such as hints). The current study advances datascience education and research by exploring the influence of AI assistance during complex datascience problem-solving. We discuss implications and identify paths for future research.
datascience is characterized by evolution: since datascience is exploratory, results evolve from moment to moment;since it can be collaborative, results evolve as the work changes hands. While existing tools help da...
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ISBN:
(纸本)9781450391573
datascience is characterized by evolution: since datascience is exploratory, results evolve from moment to moment;since it can be collaborative, results evolve as the work changes hands. While existing tools help data scientists track changes in code, they provide less support for understanding the iterative changes that the code produces in the data. We explore the idea of visualizing differences in datasets as a core feature of exploratory data analysis, a concept we call Diff in the Loop (DITL). We evaluated DITL in a user study with 16 professional data scientists and found it helped them understand the implications of their actions when manipulating data. We summarize these findings and discuss how the approach can be generalized to different datascience workflows.
A new kind of widget has begun appearing in the datascience notebook programming community that can fluidly switch its own appearance between two representations: a graphical user interface (GUI) tool and plain textu...
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ISBN:
(纸本)9781450368193
A new kind of widget has begun appearing in the datascience notebook programming community that can fluidly switch its own appearance between two representations: a graphical user interface (GUI) tool and plain textual code. data scientists of all expertise levels routinely work in both visual GUIs (data visualizations or spreadsheets) and plaintext code (numerical, data manipulation, or machine learning libraries). These work tools have typically been separate. Here, we argue for the unique role and potential of fluid GUI/text programming to serve data work practices. We contribute a generalized method and API for robust fluid GUI/text coding in notebooks that addresses key questions in code generation and user interactions. Finally, we demonstrate the potential of our method in two notebook tool examples and a usability study with professional datascience and machine learning practitioners.
We aim to increase the fexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this fexibi...
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ISBN:
(纸本)9781450375146
We aim to increase the fexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this fexibility, we extend computational notebooks with a new API mage, which supports tools that can represent themselves as both code and GUI as needed. We discuss the design of mage as well as design opportunities in the space of fexible code/GUI tools for data work. To understand tooling needs, we conduct a study with nine professional practitioners and elicit their feedback on mage and potential areas for fexible code/GUI tooling. We then implement six client tools for mage that illustrate the main themes of our study fndings. Finally, we discuss open challenges in providing fexible code/GUI interactions for data workers.
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