Coding Code: Qualitative Methods for Investigating Data Science Skills

IF 1.5 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Allison S. Theobold, Megan H. Wickstrom, Stacey A. Hancock
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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 paper we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model (Schulte, 2008) to frame our analysis, we explore two types of research questions which could be posed about students’ learning.
编码代码:调查数据科学技能的定性方法
-尽管数据科学在统计学中的重要性有所提高,但关于学生如何学习执行数据科学任务所需的计算概念和技能的研究有限。计算机科学教育工作者调查了学生如何调试自己的代码,以及学生如何通过外国代码进行推理。虽然这些研究阐明了学生编程行为或概念理解的不同方面,但尚未采用一种方法来阐明学生的学习过程。这种类型的调查需要定性的方法,它允许对学生在他们产生的计算代码中使用的技能进行整体描述,将这些描述组织成主题,并对学生或跨时间的紧急主题进行比较。在本文中,我们分享了如何概念化和实施与学生计算代码的定性编码过程。利用块模型(Schulte, 2008)来构建我们的分析,我们探索了两种类型的研究问题,这些问题可以提出关于学生学习的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Statistics and Data Science Education
Journal of Statistics and Data Science Education EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
3.90
自引率
35.30%
发文量
52
审稿时长
12 weeks
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