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1. Right but wrong: How students' mechanistic reasoning and conceptual understandings shift when designing agent‐based models using data

   https://doi.org/10.1002/sce.21890  

   Fuhrmann, Tamar; Rosenbaum, Leah; Wagh, Aditi; Eloy, Adelmo; Wolf, Jacob; Blikstein, Paulo; Wilkerson, Michelle (January 2025, Science Education)

   Abstract When learning about scientific phenomena, students are expected tomechanisticallyexplain how underlying interactions produce the observable phenomenon andconceptuallyconnect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing and refining computational models using real‐world data can support students in developing mechanistic and canonically accurate explanations of diffusion. Specifically, we examine two types of shifts in how students explain diffusion as they create and refine computational models using real‐world data: a shift towards mechanistic reasoning and a shift from noncanonical to canonical explanations. We present descriptive statistics for the whole class as well as three student work examples to illustrate these two shifts as 6th grade students engage in an 8‐day unit on the diffusion of ink in hot and cold water. Our findings show that (1) students develop mechanistic explanations as they build agent‐based models, (2) students' mechanistic reasoning can co‐exist with noncanonical explanations, and (3) students shift their thinking toward canonical explanations after comparing their models against data. These findings could inform the design of modeling tools that support learners in both expressing a diverse range of mechanistic explanations of scientific phenomena and aligning those explanations with canonical science. 

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2. Code-first learning environments for science education: a design experiment on kinetic molecular theory

   Aslan, U.; LaGrassa, N.; Horn, M.S.; Wilensky, U. (June 2020, Constructionism 2020)

   null (Ed.)

   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. 

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3. Right but Wrong: The Independence of Mechanistic Reasoning and Canonical Understanding in Studying Diffusion

   Fuhrmann, T.; Rosenbaum, L.; Eloy, A; Wagh, A.; Wolf, A.; Blikstein, P.; Wilkerson, M. (January 2023, Annual International Conference of the National Association for Research in Science Teaching (NARST))

   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. 

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4. Code-first learning environments for science education: a design experiment on kinetic molecular theory

   Aslan, U. (June 2020, Constructionism 2020)

   null (Ed.)

   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. 

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5. Exploring 6th-Grade Students Model-Based Reasoning about Energy Flow Between Societal and Earth Systems.

   Zangori, L. (March 2022, National Association of Research in Science Teaching (NARST))

   While there is an extensive literature base on the energy ideas students hold, there are few studies that examine how elementary students use scientific modeling to conceptualize the interrelationships between societal and Earth systems or how students consider the ways that societal energy systems interact with natural energy systems. This is exploratory project is situated in this space. We explored how 6th-grade students’ (aged 11 – 12) conceptualize energy flow within and across their school building and the surrounding environment within their models. Here, we report our baseline findings the students held at the start of 6th-grade prior to experiencing any energy curriculum asking how 6th-grade students models conceptualize energy flow between their school building and the surrounding environment in their models. We worked with five 6th-grade teachers from the same school district within a small Midwestern city. We collected and analyzed the data quantitatively and qualitatively. Findings highlight the students’ ideas about energy flow which includes viewing energy used in human systems as separate from energy in natural systems (such as a food chain). 

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