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We explored the COVID-19 pandemic as a context for learning about the role of science in a global health crisis. In spring 2020, at the beginning of the first pandemic-related lockdown, we worked with a high school teacher to design and implement a unit on human brain and behavior science. The unit guided her 17 students in creating studies that explored personally relevant questions about the pandemic to contribute to a citizen science platform. Pre-/postsurveys, student artifacts, and student and teacher interviews showed increases in students' fascination with science--a driver of engagement and career preference--and sense of agency as citizen scientists. Students approached science as a tool for addressing their pandemic-related concerns but were hampered by the challenges of remote schooling. These findings highlight both the opportunities of learning from a global crisis, and the need to consider how that crisis is still affecting learners.

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.

Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices.

The Next Generation Science Standards (NGSS) and other national frameworks are calling for much more sophisticated approaches to STEM education, centered around the integration of complex experimentation (including real labs, not just simulations), data collection and analysis, modeling, and data-driven argumentation, i.e., students can behave like real scientists. How to implement such complex approaches in scalable ways is an unsolved challenge - both for presential and distance education. Here we report on the iterative design and large-scale deployment of an open online course with a "biology cloud experimentation lab" (using living cells) that engaged remote learners (> 300 students) in the scientific practices of experimentation, modeling and data analysis to investigate the phototaxis of a microorganism. We demonstrate (1) the robustness and scalability of the cloud lab technology (> 2,300 experiments run), (2) the design principles and synergistic integration of multiple UI and learning activities and suitable data formats to facilitate NGSS-aligned science activities, and (3) design features that leverages the natural variability of real biology experiments to instigate authentic inquiry. This platform and course content are now suited for large-scale adaptation in formal K-16 education; and we provide recommendations for inquiry-based science learning in general.

Manipulative environments play a fundamental role in inquiry-based science learning, yet how they impact learning is not fully understood. In a series of two studies, we develop the argument that manipulative environments (MEs) influence the kind of inquiry behaviors students engage in, and that this influence realizes through the affordances of MEs, independent of whether they are physical or virtual. In particular, we examine how MEs shape college students' experimentation strategies and conceptual understanding. In study 1, students engaged in two consecutive inquiry tasks, first on mass and spring systems and then on electric circuits. They either used virtual or physical MEs. We found that the use of experimentation strategies was strongly related to conceptual understanding across tasks, but that students engaged differently in those strategies depending on what ME they used. More students engaged in productive strategies using the virtual ME for electric circuits, and vice versa using the physical ME for mass and spring systems. In study 2, we isolated the affordance of measurement uncertainty by comparing two versions of the same virtual ME for electric circuits--one with and one without noise--and found that the conditions differed in terms of productive experimentation strategies. These findings indicate that measures of inquiry processes may resolve apparent ambiguities and inconsistencies between studies on MEs that are based on learning outcomes alone.