Cloud-based science labs allow science classrooms to engage in authentic science inquiry without the logistical and procedural complexities of real hands-on laboratories. A major challenge is to understand the variation of teaching contexts and needs and the implication for technology design. Utilizing an Biology Cloud Lab (BCL) for real-time experimentation that had been deployed as Massive Open Online Course (MOOC) previously, we now execute three in-depth case studies of how K-12 teachers (with ~200 students) adapt and deploy this BCL and MOOC material for their own class-room needs. We uncovered that teachers differed in their diverse range of learning objectives (e.g., large-scale data analysis vs. computational modeling based on real data), how the teachers were able to adapt the BCL and MOOC material differently to meet their respective curricular needs, and how logistically complex and time intensive processes can be automated to allow teachers and students to focus on key practices. This work reveals the need and design rules for well-standardized and modularized cloud-based science laboratories and accompanying learning materials to then enable rich inquiry based learning activities that work at scale while also being highly customizable to the local classroom contexts.

How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types (e.g., debugging problems or Parson problems). However, few studies have examined the measurement tools of CPS competencies themselves: affordances and limitations of the measurement tools and how they compare, or whether different task types might elicit CPS competencies differently. Research needs to address these questions in order to better understand how to design robust, generalizable, and effective measurement tools for CPS competencies. This paper presents an exploratory study that contributes to this research direction. It is part of a larger international project to develop an open-access formative assessment platform for CPS, which includes a novel authoring tool for a wide range of task types for interactive block-based programming. We used the tool to create an interactive programming experience with multiple task types and gave it to more than 300 secondary school students from different countries. We also administered a validated multiple-choice measurement of Computational Thinking with block-based programs. We focused on task complexity as a characteristic of task type, using a classification scheme based on task design features. Comparing students' performances on tasks of different complexity and using two distinct measurement tools, we found that the multiple-choice measurement only partially predicts performance in the interactive programming task. Additionally, its predictive capacity varies significantly between task types of differing complexity.

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.

National guidelines advocate for a more sophisticated STEM education that integrates complex and authentic scientific practices, e.g., experimentation, data collection, data analysis, and modeling. How to achieve that is currently unclear for both presential and distance education. We recently developed a scalable cloud lab that enables many online users to perform phototaxis experiment with real, living Euglena cells (opposed to just simulations). Here we iteratively designed and deployed an open course on the edX platform including suitable user interfaces that facilitates inquiry-based learning on this cloud lab: Online students (>300) run real experiments (>2,300), performed data analysis, explored models, and even formulated and experimentally tested their own hypotheses. Platform and course content are now suited for global adaptation in formal K-16 education. We will demo our cloud lab at the conference.

This paper introduces a new environment for programming robots and physical computing devices---the Spatial Computing Platform (SCP)---and compares it to a text-based programming environment (the Cricket Logo). The SCP simplifies the process of constructing conditional statements that link the robot's inputs and outputs together. It does this by providing the user with a virtual canvas that they can draw rectangles on using the mouse. Each rectangle represents a range of sensor values, and specific outputs can be assigned to each rectangle. When the sensor values enter into the specified range, the outputs will turn on. We designed a study with 60 youth to compare this environment to Cricket Logo, a well-known variant of Logo designed to control robotic devices. We found that participants using the spatial computing platform were able to build programs of higher complexity and make more changes to their programs over the course of an hour-long workshop.

Interacting with biological systems via experiments is important for academia, industry, and education, but access barriers exist due to training, costs, safety, logistics, and spatial separation. High-throughput equipment combined with web streaming could enable interactive biology experiments online, but no such platform currently exists. We present a cloud experimentation architecture (paralleling cloud computation), which is optimized for a class of domain-specific equipments (biotic processing units - BPU) to share and execute many experiments in parallel remotely and interactively at all time. We implemented an instance of this architecture that enables chemotactic experiments with a slime mold Physarum Polycephelum. A user study in the blended teaching and research setting of a graduate-level biophysics class demonstrated that this platform lowers the access barrier for non-biologists, enables discovery, and facilitates learning analytics. This architecture is flexible for integration with various biological specimens and equipments to facilitate scalable interactive online education, collaborations, research, and citizen science.

We developed Trap it!, a human-biology interaction (HBI) medium encompassing a touchscreen interface, microscopy, and light projection. Users can interact with living cells by drawing on a touchscreen displaying the microscope view of the cells. These drawings are projected onto the microscopy field as light patterns, prompting observable movement in phototactic responses. The system design enables stable and robust HBI and a wide variety of programmed activities (art, games, and experiments). We investigated its affordances as an exhibit in a science museum in both facilitated and unfacilitated contexts. Overall, it had a low barrier of entry and fostered rich communication among visitors. Visitors were particularly excited upon realizing that the interaction involved real organisms, an understanding that was facilitated by the eyepiece on the physical system. With the results from user study, we provide our observations, insights and guidelines for designing HBI as a permanent museum exhibit.

Frameworks that create synergies across disciplines provide a powerful means for learning by relating concepts from the different fields that are usually difficult to grasp individually. We discuss the design of the BeatTable, a microworld that uses the relation between mathematics and music to engage learners in using ratios and proportions to create rhythms and learn about musical composition. The BeatTable is a physical table with a digital environment that can be controlled by tangible instruments, and through immediate auditory and visual feedback makes salient the relationships between math and music. With a low-floor, high-ceiling design philosophy, BeatTable provides learners the opportunity to build on their conceptions about music and to practice and hone their use of ratios and proportions. We present what our design choices, the technology used, and a description of initial user feedback.