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Background: While advocates for integrating Computational Thinking (CT) into existing K12 classrooms have acknowledged and aimed to address various barriers to implementation, we contend that a more foundational issue—tensions between the epistemology of computing and those of existing disciplines—has largely been overlooked. Studies of contact between heterogeneous disciplinary perspectives in both pedagogical and real world professional settings point to other risks, and harms, that educators may need to consider as they attempt to integrate CT into their teaching. As such, designing for integrated CT pedagogies does not simply require addressing functional problems such as teacher professional learning and limited classroom time, but rather implicates complex epistemological navigations. Objective: This manuscript explores epistemic tensions between Computational Thinking (CT) and K12 humanities and arts disciplines and possibilities for their resolution. Method: Based on a Delphi study with 43 experts from three disciplines—language arts, social studies, and arts—as they engaged in 20 hours of focus group conversations exploring potential approaches to integrating CT these disciplines, analysis focused on identifying perceived epistemic tensions that can arise in the context of instruction and directions for their resolution. Findings: We found 5 epistemic tensions that are explored in detail: contextual reductionism, procedural reductionism, epistemic chauvinism, threats to epistemic identities, and epistemic convergence, as well as a number of potential directions for navigating them. Implications: The study’s findings provide insights that bear on both scholarship and pedagogical design aimed at promoting substantive interdisciplinary learning with CT, and, critically, navigating potential tensions that can arise within it. 

Abstract Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is$$R$$ $R$a, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale). However, other parameters, particularly those that are scale-dependent, influence surface and interfacial properties; for example the local surface slope is critical for visual appearance, friction, and wear. The present Surface-Topography Challenge was launched to raise awareness for the need of a multi-scale description, but also to assess the reliability of different metrology techniques. In the resulting international collaborative effort, 153 scientists and engineers from 64 research groups and companies across 20 countries characterized statistically equivalent samples from two different surfaces: a “rough” and a “smooth” surface. The results of the 2088 measurements constitute the most comprehensive surface description ever compiled. We find wide disagreement across measurements and techniques when the lateral scale of the measurement is ignored. Consensus is established through scale-dependent parameters while removing data that violates an established resolution criterion and deviates from the majority measurements at each length scale. Our findings suggest best practices for characterizing and specifying topography. The public release of the accumulated data and presented analyses enables global reuse for further scientific investigation and benchmarking. 

In many discussions of the ways in which abstraction is applied in computer science (CS), researchers and advocates of CS education argue that CS students should be taught to consciously and explicitly move among levels of abstraction (Armoni Journal of Computers in Mathematics and Science Teaching, 32(3), 265–284, 2013; Kramer Communications of the ACM, 50(4), 37–42, 2007; Wing Communications of the ACM, 49(3), 33–35, 2006). In this paper, we describe one way that attention to levels of abstraction could also support learning in mathematics. Specifically, we propose a framework for using abstraction in elementary mathematics based on Armoni’s (2013) framework for teaching computational abstraction. We propose that such a framework could address an enduring challenge in mathematics for helping elementary students solve word problems with attention to context. In a discussion of implications, we propose that future research using the framework for instruction and teacher education could also explore ways that attention to levels of abstraction in elementary school mathematics may support later learning of mathematics and computer science. 

Incorporating computational thinking (CT) ideas into core subjects, such as mathematics and science, is one way of bringing early computer science (CS) education into elementary school. Minimal research has explored how teachers can translate their knowledge of CT into practice to create opportunities for their students to engage in CT during their math and science lessons. Such information can support the creation of quality professional development experiences for teachers. We analyzed how eight elementary teachers created opportunities for their students to engage in four CT practices (abstraction, decomposition, debugging, and patterns) during unplugged mathematics and science activities. We identified three strategies used by these teachers to create CT opportunities for their students: framing, prompting, and inviting reflection. Further, we grouped teachers into four profiles of implementation according to how they used these three strategies. We call the four profiles (1) presenting CT as general problem-solving strategies, (2) using CT to structure lessons, (3) highlighting CT through prompting, and (4) using CT to guide teacher planning. We discuss the implications of these results for professional development and student experiences. 

Driven by the need for students to be prepared for a world driven by computation, a number of recent educational reforms in science and mathematics have called for computational thinking concepts to be integrated into these content areas. However, in order for computational thinking (CT) to permeate K-12 education, we need to educate teachers about what CT ideas are and how they relate to what happens in their classroom on a day-to-day basis. This paper presents a toolkit to scaffold elementary teachers’ understanding of computational thinking ideas and how to integrate them into their lesson plans. 

In order to create professional development experiences, curriculum materials, and policies that support elementary school teachers to embed computational thinking (CT) in their teaching, researchers and teacher educators must under- stand ways teachers see CT as connecting to their classroom practices. Taking the viewpoint that teachers’ initial ideas about CT can serve as useful resources on which to build ed- ucational experiences, we interviewed 12 elementary school teachers to probe their understanding of six components of CT (abstraction, algorithmic thinking, automation, debug- ging, decomposition, and generalization) and how those com- ponents relate to their math and science teaching. Results suggested that teachers saw stronger connections between CT and their mathematics instruction than between CT and their science instruction. We also found that teachers draw upon their existing knowledge of CT-related terminology to make connections to their math and science instruction that could be leveraged in professional development. Teachers were, however, concerned about bringing CT into teaching due to limited class time and the difficulties of addressing high level CT in developmentally appropriate ways. We discuss these results and their implications future research and the design of professional development, sharing examples of how we used teachers’ initial ideas as the foundation of a workshop introducing them to computational thinking.