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![An authentic class, led by two teachers, in a physics laboratory classroom. The positioning sensor was contained in a badge worn by each teacher under the laboratory coat (left) [Colour figure can be viewed at wileyonlinelibrary.com]](publication/341135610/figure/fig1/AS:11431281176628085@1690215590109/An-authentic-class-led-by-two-teachers-in-a-physics-laboratory-classroom-The.png)
An authentic class, led by two teachers, in a physics laboratory classroom. The positioning sensor was contained in a badge worn by each teacher under the laboratory coat (left) [Colour figure can be viewed at wileyonlinelibrary.com]
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Lay Description
What is already known about this topic
The term “Classroom Proxemics” refers to how teachers and students use the classroom space, and its impact on learning.
A large number of teachers, particularly in higher education, receive no pedagogical training or feedback on classroom proxemics.
Little is known about how to create interfac...
Similar publications
The research area related to the use of Learning Analytics and the prediction of student performance is multidimensional; therefore, it can be explored and analyzed through different perspectives. This research addresses the relationship between pedagogical interventions based on Learning Analytics and student learning performance. The research pro...
Citations
... In addition, Healion et al. (2017) used data gathered from multimodal sources to identify and trace the physical movements of students and teachers during learning activities. In alignment with the concept of multimodal data, using sensor data and other multimodal data could aid in understanding the behaviours observed in the classrooms, such as teachers' behaviour Martinez-Maldonado et al., 2020), communication between a teacher and students (Harada et al., 2017), students' emotions (Cooper et al., 2009;Dragon et al., 2008;Gashi et al., 2018) and engagement in the classrooms (McNeal et al., 2014;Wang & Cesar, 2015). Recent research has also explored how well electrodermal activity (EDA) sensor data might offer a relevant data modality to be triangulated with self-perception measures to estimate teachers' orchestration load in the context of CSCL (Crespi et al., 2022). ...
Despite the growing interest in using multimodal data to analyse students' actions in Computers‐Supported Collaborative Learning (CSCL) settings, studying teacher's orchestration load in such settings remains overlooked. The notion of classroom orchestration, and orchestration load, offer a lens to study the implications of increasingly complex technology‐supported learning environments on teacher performance. A combination of multimodal data may aid in understanding teachers' orchestration actions and, as a result, gain insights regarding the orchestration load teachers perceive in scripted CSCL situations. Studying teacher orchestration load in CSCL helps understand the workload teachers experience while facilitating student collaboration and assists in informing design decisions for teacher supporting tools. In this paper, we collect and analyse data from different modalities (i.e. electrodermal activity, observation notes, log data, dashboard screen recordings and responses to self‐reported questionnaires) to study teachers' orchestration load in scripted CSCL. A tool called PyramidApp was used to deploy CSCL activities and a teacher‐facing dashboard was used to facilitate teachers in managing collaboration in real time. The findings of the study show the potential of multimodal data analysis in investigating and estimating the orchestration load experienced by teachers in scripted CSCL activities. Study findings further demonstrate factors emerging from multimodal data such as task type, activity duration, and number of students influenced teachers' orchestration load.
... It is commonly used to estimate the distance between devices, as signal strength typically decreases with increasing distance. By employing wireless Bluetooth-based instruments and RSSI, researchers can utilize proximity-based methods [e.g., 36]. RSSI values can be influenced by various factors, such as environmental conditions and obstacles that interfere with radio waves [e.g., 27]. ...
This study employed multimodal learning analytics (MMLA) to analyze behavioral dynamics during the ABCDE procedure in nursing education, focusing on gaze entropy, hand movement velocities, and proximity measures. Utilizing accelerometers and eye-tracking techniques, behaviorgrams were generated to depict various procedural phases. Results identified four primary phases characterized by distinct patterns of visual attention, hand movements, and proximity to the patient or instruments. The findings suggest that MMLA can offer valuable insights into procedural competence in medical education. This research underscores the potential of MMLA to provide detailed, objective evaluations of clinical procedures and their inherent complexities.
... Our work extends scholarship that has begun to consider the role of teachers' movement and use of space in the classroom. For example, prior scholarship offers ideas such as spatial pedagogy (Lim et al., 2012;Martinez-Maldonado et al., 2020;Yan et al., 2022), built pedagogy (Monahan, 2002), and methods to evaluate the alignment between physical classroom 4 SITUATING TEACHER MOVEMENT, SPACE, AND RELATIONSHIPS TO PEDAGOGY environments and pedagogy (Cleveland & Fisher, 2014;Ellis & Goodyear, 2018). These studies underscore the growing recognition of the importance of teachers' movement and use of space in teaching. ...
In conversations about pedagogy, researchers often overlook how physical space and movement shape teacher sensemaking. This article offers a comparative case study of classroom videos using a dynamic visual method to map embodied interaction called “interaction geography.” Our analysis proposes an integrative framework to study classroom interactions and teacher movement over space and time comprised of four salient characteristics within lessons: trails, landmarks, material routines, and circulation patterns. We discuss how this visual method and framework can be used and expanded by classroom researchers and teachers as a starting point to better understand teaching as a situative and spatial practice, a crucial step in characterizing responsive forms of instruction. This work has implications not only for teachers and teacher educators but also for architects, administrators, and researchers concerned with the physical design of classrooms.
... These three studies, however, do not provide a comprehensive picture of how student learning varies with classroom context and teaching practices. Generating such a picture is crucial for teacher-facing applications, for example, reflection tools [24,29]. ...
... Only temporality can distinguish between student-elicited changes in teaching or teacher-elicited changes in student engagement. Yet, distinguishing between both cases is key for effective teacher-facing analytics such as reflection tools [29]. Quantitative ethnography, which we survey next, can fulfill both requirements. ...
... There is a gap in constructing interpretable qualitative networks of behavior to understand effective teaching practice, which could be incorporated into teacher-facing dashboards [29]. The present study offers a methodology and analysis to bridge established learning constructs from tutor log data (e.g., hint use and attempts in AI tutors) with teacher spatial data and teacher practice gleaned from observation codes. ...
... Proximity-based identification methods (e.g., using the interpersonal proximity between individuals) have also been developed to model these granular x-y positioning data to frequency and duration of social interaction based on the theory of proxemics [20]. For example, the combination of an one-meter Euclidean distance criteria and a minimum of ten-second collocation duration has been developed and used in prior studies for differentiating potential meaningful interaction from unintended collocation [27,30]. These data collection and modelling innovations have made it possible to generate socio-spatial analytics regarding students' performance [52][54], collaboration [13,40,56], and teachers' spatial pedagogy [29,53]. ...
... Socio-spatial behaviours are often identified by infusing individuals' interaction and movement behaviours (e.g., co-locations, stops, and transitions) with insights from stakeholders' domain knowledge and the key features of learning designs [36]. Prior studies on socio-spatial learning analytics have shown that socio-spatial behaviours play an important role in offering insights into teachers' spatial pedagogy [36,37,57], classroom orchestration [1,3], students' social dynamics [46,55], teamwork [18,45], and academic and team performance [13,56,58]. Educational stakeholders also demonstrated profound interests in socio-spatial learning analytics, especially in providing more reliable evidence to support and encourage reflective practices [3,20]. ...
Capturing data on socio-spatial behaviours is essential in obtaining meaningful educational insights into collaborative learning and teamwork in co-located learning contexts. Existing solutions, however, have limitations regarding scalability and practicality since they rely largely on costly location tracking systems, are labour-intensive, or are unsuitable for complex learning environments. To address these limitations, we propose an innovative computer-vision-based approach -- Computer Vision for Position Estimation (CVPE) -- for collecting socio-spatial data in complex learning settings where sophisticated collaborations occur. CVPE is scalable and practical with a fast processing time and only needs low-cost hardware (e.g., cameras and computers). The built-in privacy protection modules also minimise potential privacy and data security issues by masking individuals' facial identities and provide options to automatically delete recordings after processing, making CVPE a suitable option for generating continuous multimodal/classroom analytics. The potential of CVPE was evaluated by applying it to analyse video data about teamwork in simulation-based learning. The results showed that CVPE extracted socio-spatial behaviours relatively reliably from video recordings compared to indoor positioning data. These socio-spatial behaviours extracted with CVPE uncovered valuable insights into teamwork when analysed with epistemic network analysis. The limitations of CVPE for effective use in learning analytics are also discussed.
... the Moodoo library 1 (Martinez-Maldonado et al., 2020b). Moodoo (indoor positioning metrics) consists of metrics for different aspects such as teachers' stops, teachers transitions, teacher-student interactions, proximity to classroom resources of interest, co-teaching and metrics related to focus of positional presence. ...
... This and other teaching behaviours have been studied in relation to the learning design and personal teaching preferences. This work can potentially serve as a -Maldonado et al., 2020b) foundation to create upgraded methods in data collection and visualization that, in combination with previously mentioned studies on indoor positioning, can enable evaluation of learning spaces and the activity unfolding in them. Yet, an important takeaway message from this work has also been the importance of considering teachers' and students' voices in the design of learning analytics innovations that rely on sensor data. ...
Research on learning spaces and their impact on teaching and learning has been a field of inquiry for decades. Yet, technological advances regarding data capture and analysis tools are opening both new opportunities and challenges in this area. This chapter illustrates the potential key role of multimodal learning analytics (MMLA) in advancing learning space research. In particular, the chapter presents a chronological overview of the evolution of analytical studies in physical learning spaces. The chapter also offers three detailed case studies from current research that illustrate how MMLA can enable spatial analyses to study learners and teachers actions in collaborative learning settings in ways that were not possible in the past. The case studies bring out interesting results that can be useful for informing the design of learning spaces while also emphasizing the complexity of measuring the effects of MMLA innovations on learning processes. By bringing together an overview of the evolution in this research line and current studies with their findings, the chapter highlights the increasing potential of MMLA to advance learning space research.
... Close contacts were identified using a proximity-based identification method relying on the Euclidean distances between two participants [39,40]. Two different criteria items were used, both adapted from the guidelines of the Department of Health and Human Services in Victoria [41], which have also been applied in a recent COVID-19 study with Australian children [8]. ...
... The non-intrusive and automated nature of our positioning tracking approach also ensured the authenticity of students' and teachers' close contact data, reducing the possible influence of observational effects. Consequently, results generated from these authentic data could be more representative than computer simulation studies and can motivate the punctual use of similar sensing technologies to model human behaviours in authentic physical spaces, with integrity [39]. ...
Background:
Stringent public health measures have been shown to influence the transmission of SARS-CoV-2 within school environments. We investigated the potential transmission of SARS-CoV-2 in a primary school setting with and without public health measures, using fine-grained physical positioning traces captured before the COVID-19 pandemic.
Methods:
Approximately 172.63 million position data from 98 students and six teachers from an open-plan primary school were used to predict a potential transmission of SARS-CoV-2 in primary school settings. We first estimated the daily average number of contacts of students and teachers with an infected individual during the incubation period. We then used the Reed-Frost model to estimate the probability of transmission per contact for the SARS-CoV-2 Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron variant (B.1.1.529). Finally, we built a binomial distribution model to estimate the probability of onward transmission in schools with and without public health measures, including face masks and physical distancing.
Results:
An infectious student would have 49.1 (95% confidence interval (CI) = 46.1-52.1) contacts with their peers and 2.00 (95% CI = 1.82-2.18) contacts with teachers per day. An infectious teacher would have 47.6 (95% CI = 45.1-50.0) contacts with students and 1.70 (95% CI = 1.48-1.92) contacts with their colleague teachers per day. While the probability of onward SARS-CoV-2 transmission was relatively low for the Alpha and Delta variants, the risk increased for the Omicron variant, especially in the absence of public health measures. Onward teacher-to-student transmission (88.9%, 95% CI = 88.6%-89.1%) and teacher-to-teacher SARS-CoV-2 transmission (98.4%, 95% CI = 98.5%-98.6%) were significantly higher for the Omicron variant without public health measures in place.
Conclusions:
Our findings illustrate that, despite a lower frequency of close contacts, teacher-to-teacher close contacts demonstrated a higher risk of transmission per contact of SARS-CoV-2 compared to student-to-student close contacts. This was especially significant with the Omicron variant, with onward transmission more likely occurring from teacher index cases than student index cases. Public health measures (eg, face masks and physical distance) seem essential in reducing the risk of onward transmission within school environments.
... This step involved calculating all possible pair combinations for each second. 2. A potential instance of social interaction was identified if two or more tags were within one-meter proximity of each other for more than ten consecutive seconds, as modelled in previous works (Chng et al., 2020;Martinez-Maldonado, Schulte, Echeverria, Gopalan, & Shum, 2020;Yan et al., 2021). This ten-second constraint minimises the false identification of unintended collocation, for example, when teachers are walking around during supervision or two students are passing by each other (Greenberg, Boring, Vermeulen, & Dostal, 2014). ...
Identifying students facing difficulties and providing them with timely support is one of the educator's key responsibilities. Yet, this task is becoming increasingly challenging as the complexity of physical learning spaces grows, along with the emergence of novel educational technologies and classroom designs. There has been substantial research and development work focused on identifying student social behaviours in digital platforms (eg, the learning management system) as predictors of academic progression. However, little work has investigated such relationships in physical learning spaces. This study explores the potential of using wearable trackers for the early detection of low‐progress students based on their social and spatial (socio‐spatial) behaviours at the school. Positioning data from 98 primary school students and six teachers were automatically captured over a period of eight weeks. Fourteen socio‐spatial behavioural features were extracted and processed using a set of machine learning classifiers to model students’ learning progression. Results illustrate the potential of prospectively identifying low‐progress students from these features and the importance of adapting classroom learning analytics to differences in pedagogical designs.
Practitioner notes
What is already known about this topic
Learning analytics research on predicting students’ academic progression is emerging in both digital and physical learning spaces.
Students’ social behaviours in learning activities is a key factor in predicting their academic progression.
Emerging sensing technologies can provide opportunities to study students’ real‐time social behaviours in physical learning spaces.
What this paper adds
Fourteen progression‐related socio‐spatial behavioural features are extracted from students’ physical (x‐y) positioning traces.
Predictive learning analytics that achieved 81% accuracy in prospectively identifying low‐progress students from their real‐time socio‐spatial behaviours.
Empirical evidence to support the need for classroom learning analytics to have instructional sensitivity (ie, be calibrated according to the learning design).
Implications for practice and/or policy
Sensing technologies and machine learning algorithms can be used to capture and generate valuable insights about higher‐order learning constructs (eg, performance and collaboration) from students' physical positioning traces in classrooms.
Researchers and practitioners should be cautious with generalised classification algorithms and predictive learning analytics that do not account for the pedagogical differences between different subjects or learning designs.
Researchers and practitioners should consider the potentially unforeseen ethical issues that can emerge in using sensing technologies and predictive learning analytics in authentic, physical classroom settings.
... From findings in a qualitative study (Martinez-Maldonado et al., 2020d), teachers contrasted two extreme mobility behaviours: 1) a teacher walking around the classroom mostly supervising, without engaging much with students (unfocused positional presence), and 2) a teacher focusing most of his/her attention on a small number of students or remaining only in specific spaces of the classroom (focused presence). From the x-y positioning data, the spectrum between these two extreme behaviours can be modelled based on the notion of spatial entropy (Batty et al., 2014) which has been used to measure information density in spatial data (Altieri et al., 2018). ...
... All assistants had the same partner (main teacher T1). These behaviours were identified by the main teacher in a qualitative study presented elsewhere (Martinez-Maldonado et al., 2020d), and correspond to lab sessions 4, 2 and 1, respectively. Figure 5 shows heatmaps corresponding to how these teaching assistants moved in the classroom space in Phase 2 of three LD2 classes. ...
... Moreover, there may be an intention of using the metrics to summatively assess teachers' performance. However, our previous qualitative studies found that is not desired by teachers and it may be concerning to try to judge their performance based only on positioning traces (Martinez-Maldonado et al., 2020c;Martinez-Maldonado et al., 2020d). ...
Teachers’ spatial behaviours in the classroom can strongly influence students’ engagement, motivation and other behaviours that shape their learning. However, classroom teaching behaviour is ephemeral, and has largely remained opaque to computational analysis. Inspired by the notion of Spatial Pedagogy, this paper presents a system called ‘Moodoo’ that automatically tracks and models how teachers make use of the classroom space by analysing indoor positioning traces. We illustrate the potential of the system through an authentic study with seven teachers enacting three distinct learning designs with more than 200 undergraduate students in the context of science education. The system automatically extracts spatial metrics (e.g. teacher-student ratios, frequency of visits to students’ personal spaces, presence in classroom spaces of interest, index of dispersion and entropy), mapping from the teachers’ low-level positioning data to higher-order spatial constructs. We illustrate how these spatial metrics can be used to generate a deeper understanding of how the pedagogical commitments embedded in the learning design, and personal teaching strategies, are reflected in the ways teachers use the learning space to provide support to students.
