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.

This work-in-progress proposes an approach that uses a low-cost, smartphone-based system for at-home, inquiry-driven science learning called STEM-Messaging System (SMS). SMS supports real-time, interactive, message-based science activities and is part of a broader project aimed at integrating science into children's daily lives by uncovering the science behind everyday objects via computer vision overlays. We discuss how three pedagogical principles–inquiry-based learning, culturally relevant pedagogies, and modeling-based learning–inform key design features of the system and its curricular activities. We identify tensions that surfaced from pilot studies involving students, parents, and teachers, providing examples of how pedagogical principles and practical applications influence design decisions.

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.

Designing computer models can be a valuable way for students to refine their understandings of scientific phenomena while creating and testing their hypotheses. Drawing on these ideas, we designed nine domain-specific blocks related to diffusion as a Scratch extension, which we called Diffusion Modeling Scratch Extension. In this paper, we describe the pedagogical principles that guided the design of the blocks and draw on the data from a pilot study with seven students to investigate how our design decisions impacted students' learning experiences.

Computational thinking (CT) is necessary for Science, Technology, Engineering, and Mathematics (STEM) literacy, but it can be difficult for many students to develop and it is challenging to integrate into science curricula. Here, we present a five-session curriculum where sixth-grade students programmed a Liquid Handling Robot (LHR) to conduct a science experiment while engaging in CT. We used a mixed-methods approach to assess how the curricular integration of robotics and science experimentation advances students’ CT skills and perceptions of computation in science. We identified growth in CT skills, specifically regarding Algorithmic Thinking. Students identified as key advantages of this approach the increased precision in experimental procedures, time-efficiency, and easier debugging. This course provides a proof of concept curriculum on how the implications for teaching and learning of CT can be assessed, and how CT and robotics can be brought to science classrooms, especially for chemistry and biology.

In this paper we will examine students' meta-modeling knowledge in the context of their participation in a Bifocal Modeling activity. Bifocal Modeling is an inquiry-based approach for science learning, which incorporates both physical experimentation and virtual modeling. The current study combines three separate case studies of students participating in different implementation modes of the Bifocal Modeling process. Different implementation methods require different modeling practices, and we will examine the consequences of these practices for students' meta-modeling knowledge. The concern of our investigation will be the ways that students critically evaluate scientific models and their understanding of the limitations of those models. Data suggest that model construction (as opposed to simple interaction) lead to deeper meta-modeling knowledge.

In this paper, we describe a set of user studies within the Bifocal Modeling (BM) framework. BM juxtaposes physical and computer models using sensor-based and computer modeling technologies, highlighting the discrepancies between ideal and real systems. When creating bifocal models, students build both a physical model with sensors of a given scientific phenomenon, and a computer model of the same phenomenon, connecting the two in real time with a special hardware interface. In this paper, we describe four formats for using BM in the classroom, as well as its affordances and characteristics.