Where Is the Nurse? Towards Automatically Visualising Meaningful Team Movement in Healthcare Education

Echeverria, Vanessa; Martínez-Maldonado, Roberto; Power, Tamara; Hayes, Carolyn; Shum, Simon Buckingham

doi:10.1007/978-3-319-93846-2_14

Cited by 34 publications

(38 citation statements)

References 8 publications

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“…In the context of project-based learning, Cukurova, Luckin, Millán, and Mavrikis (2018), and Spikol, Ruffaldi, Dabisias, and Cukurova (2018) collected data from learners' hand movements and head direction to predict their success in open-ended design tasks. In the area of professional development, Echeverria, Martinez-Maldonado, Power, Hayes, and Shum (2018) used sensor data to capture trainee nurses' interactions during healthcare training and created visualisations of their interaction and movements for effective reflections. From the educators' point of view, Prieto, Sharma, Kidzinski, Rodríguez-Triana, and Dillenbourg (2018) recently collected eye-tracking, audiovisual and accelerometer data of educators to help them support the management of classroom activities.…”

mentioning

confidence: 99%

Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring

Cukurova¹,

Kent²,

Luckin³

2019

Brit J Educational Tech

102

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The question: “What is an appropriate role for AI?” is the subject of much discussion and interest. Arguments about whether AI should be a human replacing technology or a human assisting technology frequently take centre stage. Education is no exception when it comes to questions about the role that AI should play, and as with many other professional areas, the exact role of AI in education is not easy to predict. Here, we argue that one potential role for AI in education is to provide opportunities for human intelligence augmentation, with AI supporting us in decision‐making processes, rather than replacing us through automation. To provide empirical evidence to support our argument, we present a case study in the context of debate tutoring, in which we use prediction and classification models to increase the transparency of the intuitive decision‐making processes of expert tutors for advanced reflections and feedback. Furthermore, we compare the accuracy of unimodal and multimodal classification models of expert human tutors' decisions about the social and emotional aspects of tutoring while evaluating trainees. Our results show that multimodal data leads to more accurate classification models in the context we studied.

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mentioning

confidence: 99%

Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring

Cukurova¹,

Kent²,

Luckin³

2019

Brit J Educational Tech

102

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“…For example, authors have used automated video analysis to model students' posture [45] and gestures [1], teacher's walking [10], interactions between teachers and students [1,53] during a lecture, and characterising the types of social interactions of students in makerspaces [15]. Wearable sensors have also been used to track teachers' orchestration tasks by using a combination of sensors (eye tracker, accelerometer, and a camera) [44] and students' mobility strategies while working in teams in the contexts of primary education [48], healthcare simulation [18] and firefighting training [51]. Some work has attempted to close the feedback loop by displaying some positioning traces back to teachers.…”

Section: Spatial Analysis and Positioning Technology In The Classroommentioning

confidence: 99%

“…Tracking systems have emerged recently, enabling the automated capture of positioning and proximity traces from authentic classrooms using wearables attached to students' shoes [48], computer-vision [1] and positioning trackers [18]. Some systems even summarise the time a teacher has spent in close proximity to a student or group of students, to raise an alarm if a threshold is reached (e.g.…”

Section: Introductionmentioning

confidence: 99%

Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

Martínez-Maldonado

Echeverria

Schulte

et al. 2020

Lecture Notes in Computer Science

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This paper presents Moodoo, a system that models how teachers make use of classroom spaces by automatically analysing indoor positioning traces. We illustrate the potential of the system through an authentic study aimed at enabling the characterisation of teachers' instructional behaviours in the classroom. Data were analysed from seven teachers delivering three distinct types of classes to + 190 students in the context of physics education. Results show exemplars of how teaching positioning traces reflect the characteristics of the learning designs and can enable the differentiation of teaching strategies related to the use of classroom space. The contribution of the paper is a set of conceptual mappings from x − y positional data to meaningful constructs, grounded in the theory of Spatial Pedagogy, and its implementation as a composable library of open source algorithms. These are to our knowledge the first automated spatial metrics to map from low-level teacher's positioning data to higher-order spatial constructs.

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“…These approaches have utilised various forms of technologies and data sources, including WiFi data [35], computer vision algorithms [1,6], thermal sensors [7], and wearable technologies [30,42]. They have also focused on a range of learning spaces, such as small fabrication rooms [9], healthcare simulation rooms [12], the library [40], laboratories [30], regular classrooms [1,42], and lecture rooms [6]. Out of these approaches, physical positioning tacking is one of the promising methods in providing fine-grained data for capturing in-class social interactions because of its high spatial-temporal precision [30].…”

Section: Introductionmentioning

confidence: 99%

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

Yan

Martínez-Maldonado

Cordoba

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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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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Where Is the Nurse? Towards Automatically Visualising Meaningful Team Movement in Healthcare Education

Cited by 34 publications

References 8 publications

Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring

Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring

Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

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

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