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

Davy Preuveneers (), Giuseppe Garofalo () and Wouter Joosen ()
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Davy Preuveneers: imec-DistriNet, KU Leuven
Giuseppe Garofalo: imec-DistriNet, KU Leuven
Wouter Joosen: imec-DistriNet, KU Leuven

Information Systems Frontiers, 2021, vol. 23, issue 1, No 10, 164 pages

Abstract: 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.

Keywords: data analytics; multi-modal engagement monitoring; privacy; cloud and edge computing; browser (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-020-09993-4

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