Exploring the Utility of Social-Network-Derived Collaborative Opportunity Temperature Readings for Informing Design and Research of Large-Group Immersive Learning Environments

Abstract

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.

Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces:

“…These studies were conducted in clinical simulations where a team of four students worked on one (Echeverria et al, 2018;Fernandez-Nieto, Martinez-Maldonado, Echeverria, et al, 2021) or multiple (Zhao et al, 2022;Yan, Martinez-Maldonado, Zhao, Dix, et al, 2023) patient beds in immersive physical simulation classrooms. Collaborative learning contexts were also investigated by eight prior studies but covered a wide range of learning spaces, including an early education centre (Saquib et al, 2018), collaborative classrooms (Martinez-Maldonado, Mangaroska, et al, 2020), secondary school classrooms (An et al, 2018), open learning spaces (Yan et al, 2021;Yan, Martinez-Maldonado, Gallo Cordoba, et al, 2022;, a school library (Riquelme et al, 2020), and an immersive exhibition centre (Mallavarapu et al, 2022). Five studies were conducted in laboratory learning contexts, such as digital fabrication labs (Chng et al, 2020) and physics labs (Martinez-Maldonado, Fernandez-Nieto et al, 2022).…”

“…Five prior studies used computer vision techniques to capture socio-spatial behaviours from video recordings. While these techniques require much fewer material resources than wearable trackers and proximity sensors (e.g., any cameras are sufficient (Mallavarapu et al, 2022)), they are less automated because human resources are required to clean and label data for the computer vision algorithms to achieve optimum accuracy (Chng et al, 2020;Ahuja et al, 2019). Li and colleagues (2023) presented the first automated computer vision techniques to capture socio-spatial behaviours from video recordings in educational settings.…”

Investigating Cognitive Network Models of Learners’ Knowledge Representations

“…For instance, Saqr and López-Pernas (2022) conducted a meta-analysis of several centrality measures in a social network of interactions in online forums to discover those with the most consistent associations with student grades. Mallavarapu et al (2022) analyzed collocation networks of participants' physical proximity in a virtual learning exhibit. Finally, Stasewitsch et al ( 2022) created a bipartite network representation of instructors and teaching projects to understand innovation diffusion in higher education.…”

“…A few papers in this special section dealt with dynamic network structures. Mallavarapu et al (2022) tracked dynamic change in collocation networks to provide a measure of "collaborative temperature" of the group over time. Malmberg et al (2022) analyzed time series psychometric networks to describe temporal patterns of self-regulation learning states among learners.…”

“…The social network then provides a snapshot of collaborative patterns in educational settings that can be used to predict academic success or model student engagement. The networks discussed in the special section in this issue depict such social networks (Saqr & López-Pernas, 2022;Mallavarapu et al, 2022;Stasewitsch et al, 2022).…”