Abstract Number:
3652
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Alyssa Hu (1), Neil Hatfield (2), Matthew Beckman (1)
Institutions:
(1) Penn State University, N/A, (2) Pennsylvania State University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Empowering students to produce insight by engaging and working with data requires that we support their building of powerful and productive ways of computational thinking. Through task-based interviews, we seek to understand the ways in which computational thinking appears as part of individuals' thinking as they engage in data-ing (data exploration, analysis, and communication) and the similarities and differences between individuals along an expert-novice continuum. We analyzed transcripts of these interviews using grounded theory techniques and models from the literature. In our results, we describe our participants' conceptualization of computational thinking, specifically highlighting the notion of trade-offs and adapting existing code. We also describe some key observations within data-ing, including participants working with the data file format, the hierarchical classification embedded in the variable names, and the construction of visualizations. After comparing our results to dimensions of existing models, we propose our own framework which highlights aspects of computational thinking, data-ing, and resource, and we consider implications for research and teaching.
Keywords:
computational thinking|data|expert-novice|statistics education|coding|
Sponsors:
Section on Statistics and Data Science Education
Tracks:
Miscellaneous
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