W04: Exploring How Novices and Experts Engage in Computational Thinking with Data

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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 with individuals across the expert-novice spectrum, we seek to understand the ways in which computational thinking appears as part of individuals’ thinking as they engage with data (data exploration, analysis, and communication) as well as the similarities and differences between individuals along the expert-novice continuum. Using grounded theory techniques and models from the literature, we analyzed interview transcripts from three undergraduate students, three graduate students, and one professional at a R1 institution, who had R programming experience and were majoring in, studying, or working in statistics or data science. 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 involving data, such as 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 highlighting aspects of computational thinking, working with data, and resources, and we consider implications for research and teaching.