Computational thinking in the era of data science

Abstract

Incorporating data thinking into computer science education.

M in CoMputational thinking: How long does it take to read a book?

“…The decisions around data collection and management relied on abstraction; for example, excluding details such as individual reading differences, focusing only on reading one page, reading out loud, etc., to help determine a sense of typical and how long it might take to read the entire book. “Adding domain knowledge can increase the problem's relevance and introduce a different kind of complexity that exists in real life” [20,p. 35].…”

“…Although the discipline of computer science brings focus to CT, processes of CT do not necessarily belong to computer science [10]. Using CT for problem solving involves both cognitive and social skills and can be explored outside of technological contexts [20]. In the primary mathematics classroom, unplugged situations often become the precursor to situations involving computers and may include a mathematical focus (eg, http://exploring patterns in binary numbers), and digital technologies present ways to cater for (Big) data in which size and dimension have no clear bounds.…”

“…The link between statistics, mathematics, and computer science has been established in the field of data science, with CT supporting data processes [20]. STEM professionals rely on statistical and CT when exploring natural phenomena through mathematical modelling; and in an attempt to prepare students for future STEM careers, educational policy initiatives and curriculum involving CT, in different subject areas, are increasing around the globe [eg, 4,7,8,12–14,16].…”

“…Problem solving from a data science perspective integrates CT and statistical thinking with the problem context‐domain thinking [20]. Outlined in Table 1 (not an exhaustive list) is the list of common cognitive skills for CT proposed by Mike and fellow researchers [20], which have been presented in an order that reflects CT in the Australian Curriculum: Mathematics and presented in the analysis. We propose that the cognitive skills listed resemble the ways of thinking and how one acts, characteristic of problem solvers participating in statistical investigation cycles [30].…”

Towards a Hermeneutics of Computational Thinking in Wing’s Approximations

“…Finally, although we are aware that beyond the period we have paid attention to, the scholarly analysis of the term, commenting as well on Wing’s conceptualization attempts, and how it fits (or it does not fit) inside Computer Science, has continued up to these days (see for example Denning and Tedre, 2022; Lodi & Martini, 2021; Mike et al, 2022), in this article we have focused on the first period, around a decade ago, where the definitions posited work as undoubted reference points due to both their solidity and their addressing of what is at the core of CT. We have not yet expanded, however, on what the mastery of CT represents cognitively or how it might be transferred to other skills: open and interdisciplinary problem-solving ability; confidence in dealing with complexity; tolerance of ambiguity; ability to find ingenious-creative solutions; critical spirit; the ability to work in a team; the ability to describe… so vindicated, discussed and debated since the construction of the concept’s first framework. Although a mastery of CT might contribute to the development or practice of these elements, they do not form part of its definition.…”