CVPE: A computer vision approach for scalable and privacy-preserving socio-spatial, multimodal learning analytics

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6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLAK 2023 Conference Proceedings - Towards Trustworthy Learning Analytics - The Thirteenth International Conference on Learning Analytics & Knowledge
EditorsIsabel Hilliger, Hassan Khosravi, Bart Rienties, Shane Dawson
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages175-185
Number of pages11
ISBN (Electronic)9781450398657
DOIs
Publication statusPublished - 2023
EventInternational Conference on Learning Analytics and Knowledge 2023 - Arlington, United States of America
Duration: 13 Mar 202317 Mar 2023
Conference number: 13th
https://dl.acm.org/doi/proceedings/10.1145/3576050 (Proceedings)
https://www.solaresearch.org/events/lak/lak23/ (Website)

Conference

ConferenceInternational Conference on Learning Analytics and Knowledge 2023
Abbreviated titleLAK 2023
Country/TerritoryUnited States of America
CityArlington
Period13/03/2317/03/23
Internet address

Keywords

  • collaborative learning
  • computer vision
  • epistemic network
  • learning analytics
  • multimodal

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