Exploring individuals’ computational thinking with data

This study investigates similarities and differences in computational thinking among individuals of varying experience levels as they engage with data. We operationalize computational thinking as the ways of thinking that individuals employ when using computational tools to solve problems. Focusing...

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Vydané v:ZDM Ročník 57; číslo 1; s. 119 - 135
Hlavní autori: Hu, Alyssa, Hatfield, Neil J., Beckman, Matthew D.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2025
Springer Nature B.V
Predmet:
Algorithms
Cognitive Processes
Communication
Communication (Thought Transfer)
Computation
Computers
Data analysis
Data science
Definitions
Education
Elementary Secondary Education
Heuristics
Interviews
Mathematicians
Mathematics
Mathematics Education
Meta Analysis
Original Paper
Planning
Problem solving
Programming languages
Psychology
Qualitative research
Secondary Education
Similarity
Software
Statistical Data
Students
Undergraduate Students
Undergraduate study
Computational thinking
Data
Data science education
Coding
Statistics education
ISSN:1863-9690, 1863-9704
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Popis
Shrnutí:This study investigates similarities and differences in computational thinking among individuals of varying experience levels as they engage with data. We operationalize computational thinking as the ways of thinking that individuals employ when using computational tools to solve problems. Focusing on computational thinking in the context of data, we conceptualize three classes of data engagement (exploration, analysis, and communication) and review existing problem-solving phases and actions in the literature. In task-based interviews, we study statistics and data science undergraduate students, graduate students, and a professional data scientist using the R programming language to address a data exploration and analysis task. Using a grounded theory approach, we engaged in constant comparison to characterize segments from our interviews, with models from the literature building our theoretical sensitivity. Our results follow our participants’ data exploration and analysis across the problem-solving phases of orienting, planning, executing, and checking. In addressing our research question, we note the differences in how participants loaded the data, explored the data, and planned for the task at hand as well as the similarities in how participants constructed a derived object. We then propose an initial framework containing problem-solving actions, resources, and methods and heuristics, highlighting the interplay between data exploration and analysis (based on orienting to the data and orienting to the task, respectively) as well as the actions of recasting data and creating derived objects.
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ISSN:1863-9690
1863-9704
DOI:10.1007/s11858-025-01669-0