Assessing Computational Thinking

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Back to Primers

Authors: Quinn Burke, Cinamon Sunrise Bailey, and Pati Ruiz
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Overview


From Angevine, C. (2017). Advancing computational thinking across K-12 education. [Blog post].

Computational thinking (CT) is increasingly being recognized as a crucial educational literacy characteristic of 21st century learning as well as a requisite skill for the 21st century economy, which relies on computing as an essential component of commerce. CT is broadly defined as a way of “solving problems, designing systems and understanding human behavior by drawing on the concepts fundamental to computer science” (Wing, 2006, p. 33).The term “computational thinking” can be dated back to the 1980s when Seymour Papert’s Mindstorms book brought to the mainstream the idea of using computers in K-12 schools as “objects to think with”. However, it was Jeannette Wing’s influential 2006 article on CT that helped spark CT as an educational imperative for schools. Since 2006, a total of forty (40) states have enacted–or are in the process of enacting–computer science (CS) standards and frameworks for their K-12 schools (Code.org, 2018). In high school, CS is typically a stand-alone course offering; however, on the K-8 levels, many states and districts are largely focusing on integrating computing into existing coursework, be it math, science, social studies, and/or language arts. With this curricular integration on K-8 levels, the goal is twofold: First, to foster children’s capacity to formulate and address problems systematically; and second, to direct and reinforce learning within existing academic disciplines through the refinement of such problem solving skills.

One of the primary challenges of computational thinking as an integrative, cross disciplinary competency is of assessment. In assessing CT, one could consider evaluating a student with regards to any or all of the three dimensions of CT:

  1. computational concepts: the fundamental concepts students engage with as they program or engage in CT oriented practices-such as algorithmic thinking, decomposition, abstraction, parallelism, and pattern generalization
  2. computational practices: the actual practices students develop as they encounter and engage with the concepts; this includes collecting and sorting data, designing and remixing computational models, debugging simulations, documenting one’s work, and collaboratively breaking down complex problems to their requisite parts
  3. computational perspectives: the perspectives students form about the world around them and about themselves as they comprehend these concepts and engage in such practices; perspectives here refers to learners’ own sense of agency and technology fluency, as well as a wider appreciation as to how systems function, why they break down and how they can be improved

Given the imperative to integrate CT into existing school subjects, especially on the K-8 levels, there are questions as to how to define CT as a skill and as a knowledge. Is CT best assessed as a series of learned concepts? How does understanding such concepts measurably inform CT practices? How does CT learning transfer across academic subject areas and how does its subject matter integration inform the dimensions of CT that can be be assessed? Alongside these pressing questions, there are then other persistent variables to consider that are characteristic of assessing any type of learning. How do teaching practices and purported learning styles inform the way CT is assessed, and how do these assessments relate to grade level expectations and expected competencies?

There is general consensus from the research (Aiken et al., 2012; Dorling, 2016; Duncan, 2018; Grover, Cooper, & Pea, 2014; Grover & Pea, 2013; Snow et al., 2012; & Wentrop et al., 2015) that computational thinking represents a more robust and practical goal for K-12 schools than the more nebulous goal of “digital literacies”, which too often is driven by for-profit companies promoting particular apps and products.

Yet there is still considerable debate over the precise meaning of computational thinking, and much of this stems directly back to this question of effective and consistent assessment. Being able to effectively assess CT within different content areas allows for a greater understanding as to how it may best be implemented into a range of academic content areas. More rigorous and systematic assessment also could inform what pedagogies better facilitate learners’ understanding of its various components. In the same manner, changes could be made to the wider school curricula in order to address changing student and workforce needs. Finally, gaining a stronger grasp on the ways and means CT is (and could be) assessed offers a sharper examination of equity of access and educational experience within schools with regards to which students are encountering such content and to the degree they are comprehending it.

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