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There is consensus that computational modeling can support integration of science with computing for student learning. However, it can be challenging for teachers to gain comfort with this relatively new practice, reconcile how it relates to science, and recognize its pedagogical value. In this paper, we illustrate how teachers working with a (serendipitous) analogy between a familiar process and a topic covered in chemistry class–frogs catching flies and reaction rates–enabled them to take up computational modeling in their science teaching. Drawing on notes from co-design sessions, teacher interviews and student worksheets, we illustrate how a focal teacher shifted from initial reluctance to taking ownership of designing and teaching a computational modeling lesson sequence. We also briefly show how her students took up this work. Finally, we reflect on the importance of leveraging teachers’ existing domain and pedagogical expertise as we bring these new practices into their classrooms. 

There is consensus that computational modeling can support integration of science with computing for student learning. However, it can be challenging for teachers to gain comfort with this relatively new practice, reconcile how it relates to science, and recognize its pedagogical value. In this paper, we illustrate how teachers working with a (serendipitous) analogy between a familiar process and a topic covered in chemistry class–frogs catching flies and reaction rates–enabled them to take up computational modeling in their science teaching. Drawing on notes from co-design sessions, teacher interviews and student worksheets, we illustrate how a focal teacher shifted from initial reluctance to taking ownership of designing and teaching a computational modeling lesson sequence. We also briefly show how her students took up this work. Finally, we reflect on the importance of leveraging teachers’ existing domain and pedagogical expertise as we bring these new practices into their classrooms. 

Modeling is a cornerstone of professional scientific practice, however, there aren’t enough opportunities for youth to leverage their own perspectives when engaging in modeling inquiry. This paper describes three design dimensions—interdisciplinarity, intermodality, & intergenerationality--of a 2-week long summer camp that leveraged theories of syncretism to integrate dance, science and computing in order to support youth contributions in modeling practices. The camp engaged 12 middle school youth, 2 scientists and 3 choreographers in adopting a complex systems lens and engaging in collaborative inquiry around the scientists’ research systems using choreographic and digital NetLogo modeling. Using discourse, video stills, and narrative description of a group that modeled spinal cord injuries, we show how these three dimensions disrupted barriers between disciplines (science & dance), modes of sense- making (movement & computation), and inequitable power dynamics (youth and adults). In the discussion, we draw out contributions to the literature particularly on scientific modeling. 

This paper explores how MoDa, an integrated computational modeling and data environment, enabled students to express their ideas about diffusion and shift them toward canonical ideas. Drawing on data from an 8-day unit with two 6th-grade science classes, we analyze students' utterances in presentations, drawings, and written responses to document their diverse ideas about diffusion We present three case studies to illustrate how engaging with computational modeling in MoDa and the unit around it enabled students to shift from non-canonical ideas towards more canonical explanations of diffusion. In particular, we identify three factors that helped in shifting students’ ideas: the availability of code blocks to represent a diverse range of ideas including non-canonical ones, consistent access to video data of the phenomenon, and model presentations to the whole class. The paper illustrates how a computational modeling tool and curriculum can make students' diverse ideas visible and shift them toward canonical explanations. 

This paper draws on a larger project in which we design for students to iteratively engage in scientific practices of computational modeling and data analysis. Here, we report on two sixth-grade science classes’ work in a unit about how ink diffuses through hot and cold water. Using interaction analysis, we analyzed what dimensions students attended to as they analyzed data, constructed computational models, and compared the two to validate their models. Our analysis led to three findings: 1. Visual cues from video data were salient to students who heavily drew on them to iterate on their models.; 2. Programming computational models raised questions about the behavior of the individual particles in the phenomenon.; and 3. The visual data made salient the contrasting conditions being modeled. However, instead of developing a single model that explained diffusion in both hot and cold water, students programmed distinct behaviors for each condition. The findings illustrate how visual data and modeling together can help students generate explanations to account for scientific phenomena and show evidence that students need explicit supports for thinking about models as providing an explanation for a range of related conditions in the system. 

Though the medium of computational modeling presents unique opportunities and challenges for science learning, little research examines how teachers can effectively support students in this work. To address this gap, we investigate how an experienced 6th grade teacher guides her students through programming computational, agent-based models of diffusion. Using interaction analysis of whole-class videos, we define a construct we call ontological alignment in which the teacher facilitates discourse to surface, highlight, connect and seek supporting or contradictory evidence for student ideas in ways that align with the level of analysis available in the modeling tool. We identify two practices reflecting this construct; the teacher 1. primes students to orient to interactions between particles and 2. strategically selects evidence to help discern between student theories. We discuss the pedagogical value of ontological alignment and suggest the identified practices as exemplary for supporting students’ learning through computational modeling. 

This study explores how the interplay between data analysis and model design shifts 6th-grade students' understanding of diffusion from simple to sophisticated mechanistic reasoning and from non-canonical to canonical ideas about diffusion. Using mixed-methods qualitative analysis, we determine students' mechanistic reasoning and ideas about diffusion at five different points in a curricular sequence using a new tool for computational modeling called MoDa. With this data, we present a framework for the relationship between students' developing mechanistic reasoning and their canonical understanding, suggesting that they develop independently. Further, we illustrate how the computational modeling environment, MoDa, used in this study pushed students' mechanistic reasoning toward sophistication. Moreover, in allowing them to explore non-canonical mechanisms, MoDa supported their convergence on canonical scientific ideas about diffusion. 

Modeling is generally recognized as the core disciplinary practice of science. Through examinations of rich learning environments which expand the boundaries of modeling and the practices connected to it, researchers are broadening what modeling means in disciplinary settings. This interactive session brings together a diverse spectrum of scholars to share the practices they have used to expand modeling, how they were used in their curriculum, and the impact they had on learning. This session will serve as a rich opportunity for discussion to help advance the state of the field around what counts as modeling and the role it can play in learning. 

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 of 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.