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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. 

Learners are being exposed to abstract skills like innovation, creativity and reasoning through collaborative open-ended problems. Most of these problems, like their real-world counterparts, have no definite starting or ending point, and have no fixed strategies to solve them. To help the learners explore the multiple perspectives of the problem solutions there is an urgent need for designing formative feedback in these environments. Unfortunately, there are barriers to us- ing existing EDM approaches to provide formative feedback to learners in these environments: (1) due to the vast so- lution space, and the lack of verifiability of the solutions it is impossible to create task and expert models, thus mak- ing the detection of the learners progress impractical; (2) formative feedback based on individual learner models does not scale well when many learners are collaborating to solve the same problem. In this work, we redefine formative feed- back as reshaping the learning environment and learners’ exploration paths by exposing/enlisting “fugues” as defined by Reitman [28]. Through a case study approach we, (1) val- idate methods to extract learners’ “fugues” from a collabora- tive open-ended museum exhibit, (2) design formative feed- back for learners and educators using these extracted fugues in real-time, (3) evaluate the impact of exposing fugues to group of learners interacting with the exhibit. 

Immersive open-ended museum exhibits promote ludic engagement and can be a powerful draw for visitors, but these qualities may also make learning more challenging. We describe our efforts to help visitors engage more deeply with an interactive exhibit's content by giving them access to visualizations of data skimmed from their use of the exhibit. We report on the motivations and challenges in designing this reflective tool, which positions visitors as a "human in the loop" to understand and manage their engagement with the exhibit. We used an iterative design process and qualitative methods to explore how and if visitors could (1) access and (2) comprehend the data visualizations, (3) reflect on their prior engagement with the exhibit, (4)plan their future engagement with the exhibit, and (5) act on their plans. We further discuss the essential design challenges and the opportunities made possible for visitors through data-driven reflection tools.