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Code-first learning entails the use of computer code to learn a concept, and creating computational models is one such effective method for learning about scientific phenomena. Many code-first learning approaches employ the visual block-based programming paradigm in order to be accessible to school children with no prior programming experience, providing them with high-level domain-specific code-blocks that encapsulate the underlying complex programming logic. However, even with the aid of visual clues and the benefit of simpler primitives like “forward” and “repeat,” many phenomena studied in classrooms such as the behavior of gas particles in Kinetic Molecular Theory (KMT) are challenging to describe in code. We hypothesized that code blocks designed from a phenomenological perspective to model the behavior of familiar objects and events would both promote students’ authoring of computational models and their ability to encode and test their beliefs within their models. We created these phenomenological blocks within a code-first gas particle sandbox and integrated it into a KMT lesson plan.Two high school teachers taught this curriculum to 121 students, from which we gathered and analyzed video footage from lesson activities and student focus groups. We found that the phenomenological blocks gave students the ability to start programming right away and to express their intuitive understanding of KMT through computational models. This exploratory study demonstrates the potential for phenomenological programming to broaden the application and accessibility of code-first computational modeling for learning scientific phenomena. 

Teaching science inquiry practices, especially the more contemporary ones, such as computational thinking practices, requires designing newer learning environments and appropriate pedagogical scaffolds. Using such learning environments, when students construct knowledge about disciplinary ideas using inquiry practices, it is important that they make connections between the two. We call such connections epistemic connections, which are about constructing knowledge using science inquiry practices. In this paper, we discuss the design of a computational thinking integrated biology unit as an Emergent Systems Microworlds (ESM) based curriculum. Using Epistemic Network Analysis, we investigate how the design of unit support students’ learning through making epistemic connections. We also analyze the teacher’s pedagogical moves to scaffold making such connections. This work implies that to support students’ epistemic connections between science inquiry practices and disciplinary ideas, it is critical to design restructured learning environments like ESMs, aligned curricular activities and provide appropriate pedagogical scaffolds. 

Next Generation Science Standards foreground science practices as important goals of science education. In this paper, we discuss the design of block-based modeling environments for learning experiences that ask students to actively explore complex systems via computer programming. Specifically, we discuss the implications of the design and selection of the types of blocks given to learners in these environments and how they may affect students’ thinking about the process of modeling and theorizing. We conclude with a discussion of some preliminary findings in this design based research to inform design principles for block-based programming of science phenomena as a medium for learning to build theory. 

Integrating computational thinking (CT) in the science classroom presents the opportunity to simultaneously broaden participation in computing, enhance science content learning, and engage students in authentic scientific practice. However, there is a lot more to learn on how teachers might integrate CT activities within their existing curricula. In this work, we describe a process of co-design with researchers and teachers to develop CT-infused science curricula. Specifically, we present a case study of one veteran physics teacher whose conception of CT during a professional development institute changed over time. We use this case study to explore how CT is perceived in physics instruction, a field that has a long history of computational learning opportunities. We also discuss how a co-design process led to the development of a lens through which to identify fruitful opportunities to integrate CT activities in physics curricula which we term computational transparency–purposefully revealing the inner workings of computational tools that students already use in the classroom. 

In the decades since Papert published Mindstorms (1980), computation has transformed nearly every branch of scientific practice. Accordingly, there is increasing recognition that computation and computational thinking (CT) must be a core part of STEM education in a broad range of subjects. Previous work has demonstrated the efficacy of incorporating computation into STEM courses and introduced a taxonomy of CT practices in STEM. However, this work rarely involved teachers as more than implementers of units designed by researchers. In The Children’s Machine, Papert asked “What can be done to mobilize the potential force for change inherent in the position of teachers?” (Papert, 1994, pg. 79). We argue that involving teachers as co-design partners supports them to be cultural change agents in education. We report here on the first phase of a research project in which we worked with STEM educators to co-design curricular science units that incorporate computational thinking and practices. Eight high school teachers and one university professor joined nine members of our research team for a month-long Computational Thinking Summer Institute (CTSI). The co-design process was a constructionist design and learning experience for both the teachers and researchers. We focus here on understanding the co-design process and its implications for teachers by asking: (1) How did teachers shift in their attitudes and confidence regarding CT? (2) What different co-design styles emerged and did any tensions arise? Generally, we found that teachers gained confidence and skills in CT and computational tools over the course of the summer. Only one teacher reported a decrease in confidence in one aspect of CT (computational modeling), but this seemed to result from gaining a broader and more nuanced understanding of this rich area. A range of co-design styles emerged over the summer. Some teachers chose to focus on designing the curriculum and advising on the computational tools to be used in it, while leaving the construction of those tools to their co-designers. Other teachers actively participated in constructing models and computational tools themselves. The pluralism of co-design styles allowed teachers of various comfort levels with computation to meaningfully contribute to a computationally enhanced constructionist curriculum. However, it also led to a tension for some teachers between working to finish their curriculum versus gaining experience with computational tools. In the time crunch to complete their unit during CTSI, some teachers chose to save time by working on the curriculum while their co-design partners (researchers) created the supporting computational tools. These teachers still grew in their computational sophistication, but they could not devote as much time as they wanted to their own computational learning. 

While the Next Generation Science Standards (NGSS) have presented computational thinking(CT)as an integral part of scientific inquiry, little work has been done to explicitly enable this connection in classrooms. We report on the efforts of one such design-based implementation research project which, with participation from local teachers, has been implementing CT infused STEM units in biology and chemistry classrooms. Using teacher reflections facilitated by an external evaluator, research field notes, and interviews, we identify possible issues of frame alignment in our implementations–that CT practices, particularly using computational models, were valued but would not enable students to gain a deeper understanding of scientific content. We then use this analysis and Schulman’s definition of teacher case knowledge to design a new element of the project that aims to enable teachers to promote collaborative scientific practice using computational models in the classroom that we call Lesson 0. We conclude with the discussion of a pilot implementation of this new lesson. 

This work aims to help high school STEM teachers integrate computational thinking (CT) into their classrooms by engaging teachers as curriculum co-designers. K-12 teachers who are not trained in computer science may not see the value of CT in STEM classrooms and how to engage their students in computational practices that reflect the practices of STEM professionals. To this end, we developed a 4-week professional development workshop for eight science and mathematics high school teachers to co-design computationally enhanced curriculum with our team of researchers. The workshop first provided an introduction to computational practices and tools for STEM education. Then, teachers engaged in co-design to enhance their science and mathematics curricula with computational practices in STEM. Data from surveys and interviews showed that teachers learned about computational thinking, computational tools, coding, and the value of collaboration after the professional development. Further, they were able to integrate multiple computational tools that engage their students in CT-STEM practices. These findings suggest that teachers can learn to use computational practices and tools through workshops, and that teachers collaborating with researchers in co-design to develop computational enhanced STEM curriculum may be a powerful way to engage students and teachers with CT in K-12 classrooms. 

Emergent Systems Microworlds (ESMs) are a special kind of computational models. Design of ESMs involves a combination of two approaches in Learning Sciences, namely agent-based modelling of complex systems and constructionism. ESMs and ESM-based curricula are frameworks for designing learning environments to foster the learning of complex scientific phenomena by engaging students in authentic scientific inquiry practices. In this paper, we discuss our approach in the context of an ESM called GenEvo that we designed for the learning of molecular genetics and evolution. We further discuss how agent-based representations and constructionist design principles mediated students’ expansive learning, as students collaboratively con- structed knowledge by engaging in authentic scientific inquiry practices. 

There has been a growing interest in the use of computer-based models of scientific phenomena as part of classroom curricula, especially models that learners create for themselves. However, while studies show that constructing computational models of phenomena can serve as a powerful foundation for learning science, this approach has struggled to gain widespread adoption in classrooms because it not only requires teachers to learn sophisticated technological tools (such as computer programming), but it also requires precious instructional time to introduce these tools to students. Moreover, many core scientific topics such as the kinetic molecular theory, natural selection, and electricity are difficult to model even with novice-friendly environments. To address these limitations, we present a novel design approach called phenomenological programming that builds on students' intuitive understanding of real-world objects, patterns, and events to support the construction of agent-based computational models. We present preliminary case studies and discuss their implications for STEM content learning and the learnability and expressive power of phenomenological programming. 

The importance of Computational Thinking (CT) as a goal of science education is increasingly acknowledged. This study investigates the effect of computationally-enriched science curriculum on students’ development of CT practices. Over the course of one school year, biology lessons featuring the exploration of NetLogo models were implemented in the classrooms of three 9th grade biology teachers at an urban public secondary school in the United States. One-hundred thirty-three biology students took both pre- and post-tests that were administered at the beginning and end of the school year. The students’ responses to relevant assessment items were coded and scored using rubrics designed to evaluate their mastery of two learning objectives relating to modeling and simulation practices. The first learning objective was to explore the relationship between a system’ s parameters and its behavior . The second learning objective was to identify the simplifications made by a model. Each item’s pre- and post-test scores were compared using a Wilcoxon signed-rank test. Results indicate a statistically significant improvement with respect to the second of the two learning objectives, suggesting that the computationally-enriched biology curriculum enhanced students’ ability to identify the simplifications made by a model.