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In the United States, the Next Generation Science Standards advocate for the integration of computational thinking (CT) as a science and engineering practice. Additionally, there is agreement among some educational researchers that increasing opportunities for engaging in computational thinking can lend authenticity to classroom activities. This can be done through introducing CT principles, such as algorithms, abstractions, and automations, or through examining the tools used to conduct modern science, emphasizing CT in problem solving. This cross‐case analysis of nine high school biology teachers in the mid‐Atlantic region of the United States documents how they integrated CT into their curricula following a year‐long professional development (PD). The focus of the PD emphasized data practices in the science teachers' lessons, using Weintrop et al.'s definition of data practices. These are: (a) creation (generating data), (b) collection (gathering data), (c) manipulation (cleaning and organizing data), (d) visualization (graphically representing data), and (e) analysis (interpreting data). Additionally, within each data practice, teachers were asked to integrate at least one of five CT practices: (a) decomposition (breaking a complex problem into smaller parts), (b) pattern‐recognition (identifying recurring similarities in data practices), (c) algorithms (the creation and use of formulas to predict an output given a specific input), (d) abstraction (eliminating detail in order to generalize or see the “big picture”), and (e) automation (using computational tools to carry out specific procedures). Although the biology teachers integrated all CT practices across their lessons, they found it easier to integrate decomposition and pattern recognition while finding it more difficult to integrate abstraction, algorithmic thinking, and automation. Implications for designing professional development experiences are discussed.

 

In an effort to deepen learning in K-12 science classrooms, there has been a national movement to integrate computational thinking (CT). The purpose of this phenomenographic study was to understand teachers’ perceptions of the function and usefulness of a task analysis and a decision tree tool designed to help them with integration. Teachers participated in a long-term professional development to improve their knowledge and application of CT and then developed lesson plans integrating CT into science investigations. To assist in the integration, teachers used the two unique tools. No one lesson plan or content area addressed all of the CT practices, but all CT practices were addressed in lessons across all four science areas. All 19 teachers found that the task analysis tool helped them to shift their lessons to a student-centered focus and helped them pinpoint data practices so they could systematically integrate CT practices. However, they expressed confusion over the amount of detail to document on the tool. Similarly, teachers found both benefits and barriers to the decision tree tool. Teachers found the decision tree tool to be useful in predicting the ways students may misunderstand a data practice and in reflecting on the level of accomplishment of students. However, teachers were sometimes uncertain with how to efficiently document complex student behaviors when engaged with data practices and CT. Implications for the use of the two lesson planning tools is discussed. 

The primary purpose of this study was to systematically review the literature regarding the characteristics, use, and implementation of an emerging assessment methodology, SRL microanalysis. Forty-two studies across diverse samples, contexts, and research methodologies met inclusion criteria. The majority of studies used microanalysis to either comprehensively address all three phases of SRL (i.e., forethought, performance, or reflection) or to conduct in-depth analyses of one particular phase. Microanalysis has also been used across myriad domains (e.g., academic, athletic, clinical) and tasks (e.g., mathematics problem solving, basketball shooting, diagnostic reasoning) with samples encompassing elementary to graduate school. Although SRL microanalysis has typically been used to differentiate intervention conditions or existing groups (e.g., expert vs. novice), it has increasingly been used as a diagnostic tool to inform instructional and intervention planning. Additional information regarding the types of validity addressed in the studies are discussed, as well as implications for research and school practice. 

When conducting a science investigation in biology, chemistry, physics or earth science, students often need to obtain, organize, clean, and analyze the data in order to draw conclusions about a particular phenomenon. It can be difficult to develop lesson plans that provide detailed or explicit instructions about what students need to think about and do to develop a firm conceptual understanding, particularly regarding data analysis. This article demonstrates how computational thinking principles and data practices can be merged to develop more effective science investigation lesson plans. The data practices of creating, collecting, manipulating, visualizing, and analyzing data are merged with the computational thinking practices of decomposition, pattern recognition, abstraction, algorithmic thinking, and automation to create questions for teachers and students that help them think through the underlying processes that happen with data during high school science investigations. The questions can either be used to elaborate lesson plans or embedded into lesson plans for students to consider how they are using computational thinking during their data practices in science.