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Introductory computer science courses for non-majors (CS0) aim to increase diversity and highlight the relevance of computing across disciplines. To enhance the accessibility and engagement of CS0, researchers have explored contextualized computing, where computing is integrated with another subject, to teach course content. While research has explored various designs for contextualized courses, we know less about how contextualized computing tasks impact students’ learning experiences. Through the lens of metacognition and affect, we conducted a secondary qualitative analysis on daily diary and retrospective interview data from 20 students in a CS0 course that applied coding to different contexts. Our findings demonstrate that students’ feeling of knowing and their perception of the task are two central themes that shape their affect and interest in the course. We conclude with design suggestions for contextualized computing in CS0 to better support students.
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Coding Code: Qualitative Methods for Investigating Data Science Skills
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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- PAR ID:
- 10570120
- Publisher / Repository:
- Taylor & Francis Online
- Date Published:
- Journal Name:
- Journal of Statistics and Data Science Education
- Volume:
- 32
- Issue:
- 2
- ISSN:
- 2693-9169
- Page Range / eLocation ID:
- 161 to 173
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/26939169.2023.2277847
