Where is the teacher? Digital analytics for classroom proxemics

Abstract

The term Classroom Proxemics refers to how teachers and students use classroom space, and the impact of this and the spatial design on learning and teaching. This study addresses the divide between, on the one hand, substantial work on proxemics based on classroom observations and, on the other hand, emerging work to design automated feedback that helps teachers identify salient patterns in their use of the classroom space. This study documents how digital analytics were designed in service of a senior teacher's practice‐based inquiry into classroom proxemics. Indoor positioning data from four teachers were analysed, visualized and used as evidence to compare three distinct learning designs enacted in a physics classroom. This study demonstrates how teachers can make effective use of such visualizations, to gain insight into their classroom practice. This is evidenced by (a) documenting teachers' reflections on visualizations of positioning data, both their own and that of peers and (b) identifying the types of indicator (operationalized as analytical metrics) that foreground the most useful information for teachers to gain insight into their practice.

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

“…At present, researchers have been utilizing eye‐trackers , cameras , and wearable devices presented in the instrument dimension to generate multimodal data highlighted in the data dimension . Using these devices, researchers can collect data such as heart rate , gaze , electrophysiological activity of the brain , or facial expressions data , to study and model learning strategies (Mangaroska et al, 2018; Worsley & Blikstein, 2015), to predict high‐level constructs such as learner attention and engagement (Chan et al, 2020), to design multimodal learning interfaces (Echeverria et al, 2019), or to generate insights about teaching at a more fine‐grained levels (Martinez‐Maldonado, Echeverria, et al, 2020; Martinez‐Maldonado, Schulte, et al, 2020; Prieto et al, 2018).…”

“…It has been proposed that emerging multimodal learning analytics (MLA) have the potential to enable the automated generation of models that account for the complexity of the learning processes with the purpose of providing real‐time feedback or developing MLA interfaces (Blikstein, 2013; Blikstein & Worsley, 2016; Drachsler & Schneider, 2018; Mangaroska et al, 2019). For example, some MLA studies have focused on modelling student gaze to identify group synchrony as a proxy of collaboration effectiveness (Schneider, 2020) or to orchestrate basic behaviour regulation (Mangaroska et al, 2018); capturing physiological cues to investigate group regulation strategies (Noroozi et al, 2019) and individual achievement (Pijeira‐Díaz et al, 2018); using computer vision systems to identify incorrect postures in healthcare training (Di Mitri, 2019); creating hand tracking algorithms to predict group work quality (Spikol et al, 2018); and using positioning trackers to identify teaching strategies in the physical classroom (Martinez‐Maldonado, Schulte, et al, 2020).…”

Footprints at School: Modelling In-class Social Dynamics from Students’ Physical Positioning Traces

“…For example, Chng et al, [9] classified instances of social interaction by detecting when two students were collocated within one-meter proximity of each other. Martinez-Maldonado et al, [31] also applied the one-meter proximity rule to estimate the time teachers spend with different groups of students in a physical classroom. As illustrated in these studies, a methodological application of proxemics could potentially capture in-class social interactions in a non-intrusive, real-time, and scalable manner [9,28].…”

“…However, no clear proximity rules have been established for school children sharing the same learning space. Therefore, based on the findings by Sorokowska et al [44], and the proximity values used in previous educational studies [9,31], the current study adjusted the personal (0.46 -1.00 m) and social distances (1.00 -2.10 m), respectively. 4) Identifying potential interaction.…”

Collaboration Analytics — Current State and Potential Futures

“…We argue that for the case of collaboration analytics, it would be even more risky trying to find causal relationships in incomplete data about such a complex phenomenon. Yet, even if we are able to create better predictive models based on more data traces, this would also increase the complexity of (i) the underlying collaboration models and (ii) the complexity of user interfaces for educators and students (Martinez-Maldonado, Schulte, Echeverria, Gopalan, & Buckingham Shum, 2020).…”