Programming computational models is a key practice that supports both scientific and computational learning. Block-based, domain-specific programming environments have made this practice more accessible but may constrain what students can design compared to general-purpose or text-based languages. This poster reports on the classroom use of block unpacking—a feature that allows high-level primitives to be opened, inspected, and modified—within a domain-specific modeling environment. We implemented unpacking in a four-day high school biology unit on eutrophication, where students programmed models to represent interactions among algae, bacteria, fish, nutrients, and oxygen. From the 16 students who participated, we analyzed 36 unpacking modifications made by five focal students, classifying each as a parameter change or a code structure change, and examining whether these modifications were informed by scientific reasoning. Our findings show that unpacking let students move beyond fixed domain-specific primitives, producing diverse and more expressive models. Some introduced new mechanisms, such as photosynthesis, while others focused on optimizing or debugging simulations. Importantly, more complex edits did not always correspond to deeper scientific reasoning. This study highlights unpacking as a mid-level design strategy that can expand student agency and flexibility in block-based modeling. We discuss implications for designing programming environments and instructional supports that balance ease of entry with opportunities to engage meaningfully in computing practices.

Coordinating modeling and real-world data is central to building scientific theories. This paper examines how a complementary focus on modeling and data contributed to 8th grade students’ learning of mechanisms underlying wildfire smoke spread in MoDa, a web-based environment that integrates computational modeling side-by-side with real-world data for comparison and validation. Epistemic network analysis of student responses in pre-post tests revealed a shift from primarily macro-level explanations to explanations that integrated macro and micro-level explanations of the phenomenon. Video data analysis revealed three design elements that contributed to student learning: Naming of the blocks, match between data and model visualization, and collective reflections on models. We reflect on implications for the design of environments that integrate computational modeling with real-world data analysis.

This paper presents Frog Pond, an interactive code-first learning environment about biological evolution. We deployed Frog Pond as part of a six-day curricular unit on natural selection implemented in six 7th grade science classes. Here we describe a case study of two students, Charlie and Aaron who participated in the unit. Comparing pre- and post- interviews in which they were asked to design a program for a hypothetical simulation of evolution, we found that both students shifted from an event-based programming approach to a rule-based approach. Both students also drew upon their experience with Frog Pond to explain an evolutionary phenomenon. However, the level of sophistication of the two students' explanations varied along with the aspects of Frog Pond they drew upon. These findings have implications for design improvement to better support students' understanding of evolution.

Understanding processes of evolution and natural selection is both important and challenging for learners. We describe a "codefirst" learning environment called Frog Pond designed to introduce natural selection to elementary and middle school aged learners. Learners use NetTango, a blocksbased programming interface to NetLogo, to control frogs inhabiting a lily pond. Simple programs result in changes to the frog population over successive generations. Our approach foregrounds computational thinking as a bridge to understanding evolution as an emergent phenomenon.