Schools are increasingly becoming into complex learning spaces where students interact with various physical and digital resources, educators, and peers. Although the field of learning analytics has advanced in analysing logs captured from digital tools, less progress has been made in understanding the social dynamics that unfold in physical learning spaces. Among the various rapidly emerging sensing technologies, position tracking may hold promises to reveal salient aspects of activities in physical learning spaces such as the formation of interpersonal ties among students. This paper explores how granular x-y physical positioning data can be analysed to model social interactions among students and teachers. We conducted an 8-week longitudinal study in which positioning traces of 98 students and six teachers were automatically captured every day in an open-plan public primary school. Positioning traces were analysed using social network analytics (SNA) to extract a set of metrics to characterise students' positioning behaviours and social ties at cohort and individual levels. Results illustrate how analysing positioning traces through the lens of SNA can enable the identification of certain pedagogical approaches that may be either promoting or discouraging in-class social interaction, and students who may be socially isolated. CCS CONCEPTS• Applied computing → Collaborative learning; Computer-assisted instruction; Learning management systems.

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Effective teamwork is critical to improve patient outcomes in healthcare. However, achieving this capabilityrequires that pre-service nurses develop the spatial abilities they will require in their clinical placements, suchas: learning when to remain close to the patient and to other team members; positioning themselves correctlyat the right time; and deciding on specific team formations (e.g. face-to-face or side-by-side) to enable effectiveinteraction or avoid disrupting clinical procedures. However, positioning dynamics are ephemeral and caneasily become occluded by the multiple tasks nurses have to accomplish. Digital traces automatically capturedby indoor positioning sensors can be used to address this problem for the purpose of improving nurses' reflection, learning and professional development. This paper presents; i) a qualitative study that illustrateshow to elicit spatial behaviours from educators' pedagogical expectations, and ii) a modelling approachthat transforms nurses' low-level position traces into higher-order proxemics constructs, informed by sucheducatos' expectations, in the context of simulation-based teamwork training. To illustrate our modellingapproach, we conducted an in-the-wild study with 55 undergraduate students and five educators from whompositioning traces were captured in eleven authentic nursing education classes. Low-levelx-ydata was usedto model three proxemic constructs: i) co-presence in interactional spaces, ii) socio-spatial formations (i.e.f-formations), and ii) presence in spaces of interest. Through a number of vignettes, we illustrate how indoorpositioning analytics can be used to address questions that educators and researchers have about teamwork inhealthcare simulation settings.

Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.

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