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  1. 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 challengingmore »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.« less
  2. Kong, S.C. (Ed.)
    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 understandingmore »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.« less
  3. Kong, S.C. (Ed.)
  4. 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 collaborativelymore »con- structed knowledge by engaging in authentic scientific inquiry practices.« less
  5. 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 ofmore »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.« less
  6. Kong, S.C. (Ed.)
    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,more »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.« less
  7. Gresalfi, M. ; Horn, I. S. (Ed.)
    There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the fieldmore »of STEM and CT integration.« less
  8. Gresalfi, M. ; Horn, I. S. (Ed.)
    There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the fieldmore »of STEM and CT integration.« less
  9. In response to increasing calls to include computational thinking (CT) in K-12 education, some researchers have argued for integrating science learning and CT. In that vein, this paper investigates conceptual learning and computational practices through the use of a code-first modeling environment called Frog Pond in a middle school classroom. The environment was designed to enable learners to explore models of evolutionary shifts through domain-specific agent-based visual programming. It was implemented as a curricular unit in seventh grade science class. We analyzed video and log data of two contrasting student pairs. This paper presents one of our findings: Development ofmore »modular core functional code-units or what we call anchor code. Anchor code is a body of code that creates a stable base from which further explorations take place. We argue that anchor code is evidence for conceptual learning and computational practices.« less