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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. 

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 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. 

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. 

Data science has increasingly integrated sociocritical theories and approaches, helping youth to not only learn data science but also relate data to their everyday and sociohistorical lives. Our project, Writing Data Stories, furthers these efforts by exploring sociocritical data literacies in a large-scale classroom enactment. We examine trends in middle school science student groups’ (n=11) data participation and sociocritical participation, showing how these forms of participation ebb and flow across a 21-day unit. We then present focal group case studies to further unpack how participation shifted over time and suggest what factors contributed to these shifts. We found that data participation was affected by the tools at students’ disposal, and sociocritical participation was shaped by the questions groups asked of each other and the data. These findings suggest that special attention to tools and guiding questions is critical when designing for sociocritical data literacy in middle school science contexts. 

I've already deposited this and this record is a duplicate. I apparently can't move on with the project report unless I submit a duplicate for some reason. 

The projects in this interactive poster symposium explore ways of engaging learners with social justice issues through the design and study of data literacy interventions. These interventions span classroom to museum contexts, and environmental to social sciences domains. Together, they illustrate research and practice approaches for engaging learners with data to promote emancipatory activity. 

The design of most learning environments focuses on supporting students in making, constructing, and putting together projects on and off the screen, with much less attention paid to the many issues—problems, bugs, or traps—that students invariably encounter along the way. In this symposium, we present different theoretical and disciplinary perspectives on understanding how learners engage in debugging applications on and off screen, examine learners’ mindsets about debugging from middle school to college students and teachers, and present pedagogical approaches that promote strategies for debugging problems, even having learners themselves design problems for others. We contend that learning to identify and fix problems—debug, troubleshoot, or get unstuck—in completing projects provides a productive space in which to explore multiple theoretical perspectives that can contribute to our understanding of learning and teaching critical strategies for dealing with challenges in learning activities and environments.