Assessing computational thinking: an overview of the field

The last decade has seen rapid growth in the presence of computational thinking (CT) in educational contexts. Those working to advance CT argue that the concepts and skills associated with CT are essential to succeed in an increasingly computational world. As a result of these efforts, CT has a growing presence in K-12 classrooms and beyond. This can be seen in the inclusion of CT in disciplinary standards (e.g. the Next Generation Science Standards and Common Core Math identifying CT as a core practice), as well as national curricular efforts (e.g. the United Kingdom’s national computing curriculum seeks to have students “develop and apply their analytic, problem-solving, design, and computational thinking skills”). Just as CT has a growing presence in formal education, it can also be seen in informal contexts through the growth of computing camps, after-school and library CT programming, and a growing array of toys designed to engage youth with CT. The contemporary discussion around CT began with Wing’s (2006) article, where she argued “to reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability” (p. 33). However, the conceptual origins have a much longer history, dating back to early work on the Logo programming language and Papert’s insights on the potential of computing as a powerful context for learning (1980). In response to Wing’s article, much effort has been dedicated to trying to define what constitutes CT and where the boundaries of the construct lie. While the community has yet to settle on a single unified definition, there is general consensus that CT includes foundational computing concepts such as abstraction and algorithms, as well as computing practices such as problem decomposition and debugging (Grover & Pea, 2013; Shute et al., 2017). As the dust started to settle from early debates around the scope and nature of CT, a growing number of research projects sought to design CT learning experiences. Spurred in part by an increase in funding for educational projects at the intersection of computing and other disciplines, a space in which CT is particularly well-suited to contribute, the last decade has seen tremendous growth in curricula, learning environments, and innovations around CT education (Tang et al., 2020). In the wake of this growth, this special issue seeks to respond to a question of growing importance: How do we assess computational thinking? This is not a straightforward question to answer as several aspects of CT make it challenging to assess. For example, there is a wide variety of methods by which CT is taught and contexts in which students learn CT. While some schools offer stand-alone CT learning experiences, other schools may try to integrate CT within current subject matters. Further, as discussed above, CT is a relatively ill-defined construct, thus, different assessments may focus on slightly different dimensions of CT. Collectively, this produces a landscape where a variety of assessments are needed to reflect the different conceptual, contextual, and motivational aspects of CT instruction. COMPUTER SCIENCE EDUCATION 2021, VOL. 31, NO. 2, 113–116 https://doi.org/10.1080/08993408.2021.1918380