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Cloud and edge based data analytics for privacy-preserving multi-modal engagement monitoring in the classroom

Author

Listed:
  • Davy Preuveneers

    (imec-DistriNet, KU Leuven)

  • Giuseppe Garofalo

    (imec-DistriNet, KU Leuven)

  • Wouter Joosen

    (imec-DistriNet, KU Leuven)

Abstract

Learning management systems are service platforms that support the administration and delivery of training programs and educational courses. Prerecorded, real-time or interactive lectures can be offered in blended, flipped or fully online classrooms. A key challenge with such service platforms is the adequate monitoring of engagement, as it is an early indicator for a student’s learning achievements. Indeed, observing the behavior of the audience and keeping the participants engaged is not only a challenge in a face-to-face setting where students and teachers share the same physical learning environment, but definitely when students participate remotely. In this work, we present a hybrid cloud and edge-based service orchestration framework for multi-modal engagement analysis. We implemented and evaluated an edge-based browser solution for the analysis of different behavior modalities with cross-user aggregation through secure multiparty computation. Compared to contemporary online learning systems, the advantages of our hybrid cloud-edge based solution are twofold. It scales up with a growing number of students, and also mitigates privacy concerns in an era where the rise of analytics in online learning raises questions about the responsible use of data.

Suggested Citation

  • Davy Preuveneers & Giuseppe Garofalo & Wouter Joosen, 2021. "Cloud and edge based data analytics for privacy-preserving multi-modal engagement monitoring in the classroom," Information Systems Frontiers, Springer, vol. 23(1), pages 151-164, February.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:1:d:10.1007_s10796-020-09993-4
    DOI: 10.1007/s10796-020-09993-4
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    Cited by:

    1. Longling Zhang & Bochen Shen & Ahmed Barnawi & Shan Xi & Neeraj Kumar & Yi Wu, 2021. "FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia," Information Systems Frontiers, Springer, vol. 23(6), pages 1403-1415, December.
    2. John Oredo & Denis Dennehy, 2023. "Exploring the Role of Organizational Mindfulness on Cloud Computing and Firm Performance: The Case of Kenyan Organizations," Information Systems Frontiers, Springer, vol. 25(5), pages 2029-2050, October.
    3. Georgios L. Stavrinides & Helen D. Karatza, 2024. "Security, Cost and Energy Aware Scheduling of Real-Time IoT Workflows in a Mist Computing Environment," Information Systems Frontiers, Springer, vol. 26(4), pages 1223-1241, August.

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