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Coding Code: Qualitative Methods for Investigating Data Science Skills

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Title: Coding Code: Qualitative Methods for Investigating Data Science Skills
Language: English
Authors: Allison S. Theobold (ORCID 0000-0002-8092-6182), Megan H. Wickstrom (ORCID 0000-0002-0557-0112), Stacey A. Hancock (ORCID 0000-0002-8540-2492)
Source: Journal of Statistics and Data Science Education. 2024 32(2):161-173.
Availability: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 13
Publication Date: 2024
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Computer Science Education, Coding, Data Science, Statistics Education, Skill Development, Learning Processes, Data Collection, Data Analysis, Programming, Programming Languages, Syntax
DOI: 10.1080/26939169.2023.2277847
ISSN: 2693-9169
Abstract: Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students' programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students' learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students' computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students' learning. Supplementary materials for this article are available online.
Abstractor: As Provided
Notes: https://github.com/atheobold/QDA-tutorial-website
Entry Date: 2024
Accession Number: EJ1418483
Database: ERIC
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  Data: Coding Code: Qualitative Methods for Investigating Data Science Skills
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  Data: <searchLink fieldCode="AR" term="%22Allison+S%2E+Theobold%22">Allison S. Theobold</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8092-6182">0000-0002-8092-6182</externalLink>)<br /><searchLink fieldCode="AR" term="%22Megan+H%2E+Wickstrom%22">Megan H. Wickstrom</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0557-0112">0000-0002-0557-0112</externalLink>)<br /><searchLink fieldCode="AR" term="%22Stacey+A%2E+Hancock%22">Stacey A. Hancock</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8540-2492">0000-0002-8540-2492</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Statistics+and+Data+Science+Education%22"><i>Journal of Statistics and Data Science Education</i></searchLink>. 2024 32(2):161-173.
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  Data: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students' programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students' learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students' computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students' learning. Supplementary materials for this article are available online.
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  Data: https://github.com/atheobold/QDA-tutorial-website
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        Type: general
      – SubjectFull: Coding
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      – SubjectFull: Data Science
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      – SubjectFull: Statistics Education
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      – SubjectFull: Skill Development
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      – SubjectFull: Learning Processes
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      – SubjectFull: Data Collection
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      – SubjectFull: Data Analysis
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      – SubjectFull: Programming
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      – SubjectFull: Syntax
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      – TitleFull: Coding Code: Qualitative Methods for Investigating Data Science Skills
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