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Not AvailableEngaging with computational models is central to both scientific and computational learning. A promising approach to “lower the floor” and make computational modeling more accessible is the development of domain-specific and block-based environments, which reduce programming complexity while leveraging students’ intuitions about scientific ideas. To balance usability and expressiveness in these environments, we develop the feature of “unpacking” blocks, allowing users to open and modify high-level blocks into the simpler constituent elements that define them. In this study, we analyze high school students’ models, screen recordings, and artifact-based interviews to investigate their motivation for modifying domain-specific blocks for eutrophication in aquatic ecosystems. We found that unpacking and modifying blocks supported students in both exploring scientific ideas and addressing specific goals of computational modeling, providing insights on how unpacking domain-specific blocks can support both computing and science learning. 

Not AvailableAn emerging body of work in the learning sciences has examined how computational models can support teachers in responding to students' prompts, inquiry, and ideas. This work has highlighted how teachers make discursive moves in relation to computational models to support classroom discussion. In this paper, we focus on a complementary phenomenon: teachers' design of code reflections, or curricular modifications that deepen students' engagement with one another's code for scientific and computational sensemaking. We highlight how these code reflections advanced student discourse and how both the code reflections and discourse became more sophisticated over time, shifting towards making connections across code, behaviors, simulation outcomes, data and the scientific process being represented. We reflect on how this progression was driven by shifts in the teachers’ comfort with code and computational modeling and the resources designers can offer to educators to support the development of code reflections. 

Computational modeling tools present unique opportunities and challenges for student learning. Each tool has a representational system that impacts the kinds of explorations students engage in. Inquiry aligned with a tool’s representational system can support more productive engagement toward target learning goals. However, little research has examined how teachers can make visible the ways students’ ideas about a phenomenon can be expressed and explored within a tool’s representational system. In this paper, we elaborate on the construct of ontological alignment—that is, identifying and leveraging points of resonance between students’ existing ideas and the representational system of a tool. Using interaction analysis, we identify alignment practices adopted by a science teacher and her students in a computational agent-based modeling unit. Specifically, we describe three practices: (1) Elevating student ideas relevant to the tool’s representational system; (2) Exploring and testing links between students’ conceptual and computational models; and (3) Drawing on evidence resonant with the tool’s representational system to differentiate between theories. Finally, we discuss the pedagogical value of ontological alignment as a way to leverage students’ ideas in alignment with a tool’s representational system and suggest the presented practices as exemplary ways to support students’ computational modeling for science learning. 

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. 

In the last decade, HCI researchers have designed and engineered several systems to lower the entry barrier for beginners and support novices in learning hands-on creative maker skills. These skills range from building electronics to fabricating physical artifacts. While much of the design and engineering of current learning systems is driven by the advances in technology, we can reimagine these systems by reorienting the design goals around constructivist and sociocultural theories of learning to support learning progression, engagement across artistic disciplines, and designing for inclusivity and accessibility. This one-day workshop aims to bring together the HCI researchers in systems engineering and learning sciences, challenge them to reimagine the future design of systems of learning creative maker skills, form connections across disciplines, and promote collaborative research in the systems of learning creative skills. 

Haptic force feedback systems are unique in their ability to dynamically render physical representations. Although haptic devices have shown promise for supporting learning, prior work mainly describes results of haptic-supported learning without identifying underlying learning mechanisms. To this end, we designed a haptic-supported learning environment and analyzed four students who used it to make connections between two different mathematical representations of sine and cosine: the unit circle, and their graph on the Cartesian plane. We highlight moments where students made connections between the representations, and identify how the haptic feedback supported these moments of insight. We use this evidence in support of a proposed theoretical and design framework for educational haptics. This framework captures four types of haptic representations, and focuses on one -- the haptic bridge -- that effectively scaffolds sense-making with multiple representations. 

Haptic technology has the potential to expand and transform the ways that students can experience a variety of science, technology, engineering, and math (STEM) topics. Designing kinesthetic haptic devices for educational applications is challenging because of the competing objectives of using low-cost components, making the device robust enough to be handled by students, and the desire to render high fidelity haptic virtual environments. In this paper, we present the evolution of a device called "Hapkit": a low cost, one-degree-of-freedom haptic kit that can be assembled by students. From 2013-2015, different versions of Hapkit were used in courses as a tool to teach haptics, physics, and control. These include a Massive Open Online Course (MOOC), two undergraduate courses, a graduate course, and a middle school class. Based on our experience using Hapkit in these educational environments, we evolved the design in terms of its structural materials, drive mechanism, and mechatronic components. Our latest design, Hapkit 3.0, includes several features that allow students to manufacture and assemble a robust and high-fidelity haptic device. First, it uses 3-D printed plastic structural material, which allows the design to be built and customized using readily available tools. Second, the design takes into account the limitations of 3-D printing, such as warping during printing and poor tolerances. This is achieved at a materials cost of approximately US $50, which makes it feasible for distribution in classroom and online education settings. The open source design is available at http://hapkit.stanford.edu.