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Large-group (n > 8) co-located collaboration has not been adequately studied because it demands different conceptual framings than those used to study small-group collaboration, while also posing pragmatic constraints on data collection. Working within these pragmatic constraints, we use video data to devise an indicator of the current possibilities for learner collaboration during large-group co-located interactions. We borrow conceptualizations from proxemics and social network analysis to construct collaborative opportunity networks, allowing us to define the concept of collaborative opportunity temperature (COT) readings: a “snapshot” of the current configuration of the different social subgroup structures within a large group, indicating emergent opportunities for collaboration (via talk or shared action) due to proximity. Using a case study of two groups of people (n = 11, n = 12) who interacted with a multi-user museum exhibit, we outline the processes of deriving COT. We show how to quickly detect differences in subgroup configurations that may result from educational interventions and how COT can triangulate with and complement other forms of data (audio transcripts and activity logs) during lengthier analyses. We also outline how COT readings can be used to supply formative feedback on social engagement to learners and be adapted to other learning environments. 

We examined the regulation of shared problem solving in a museum exhibit. We found that we had to augment our dialogue codes to properly embrace the dynamic nature of the observed learning regulation. These changes reflect aspects of shared regulation that occur when learning takes place (1) in an immersive open-ended learning environment, where (2) learners work together in large groups. We present preliminary results, arguing that designers and researchers may benefit from recognizing how planning and evaluation acts can be tactically embedded in immersive learning environments. 

The Next Generation Science Standards and the National Research Council recognize systems thinking as an essential skill to address the global challenges of the 21st century. But the habits of mind needed to understand complex systems are not readily learned through traditional approaches. Recently large-scale interactive multi-user immersive simulations are being used to expose the learners to diverse topics that emulate real-world complex systems phenomena. These modern-day mixed reality simulations are unique in that the learners are an integral part of the evolving dynamics. The decisions they make and the actions that follow, collectively impact the simulated complex system, much like any real-world complex system. But the learners have difficulty understanding these coupled complex systems processes, and often get “lost” or “stuck,” and need help navigating the problem space. Formative feedback is the traditional way educators support learners during problem solving. Traditional goal-based and learner-centered approaches don’t scale well to environments that allow learners to explore multiple goals or solutions, and multiple solution paths (Mallavarapu & Lyons, 2020). In this work, we reconceptualize formative feedback for complex systems-based learning environments, formative fugues, (a term derived from music by Reitman, 1964) to allow learners to make informed decisions about their own exploration paths. We discuss a novel computational approach that employs causal inference and pattern matching to characterize the exploration paths of prior learners and generate situationally relevant formative feedback. We extract formative fugues from the data collected from an ecological complex systems simulation installed at a museum. The extracted feedback does not presume the goals of the learners, but helps the learners understand what choices and events led to the current state of the problem space, and what paths forward are possible. We conclude with a discussion of implications of using formative fugues for complex systems education.