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Information Systems Frontiers

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25-02-2020

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

Authors: Davy Preuveneers, Giuseppe Garofalo, Wouter Joosen

Published in: Information Systems Frontiers | Issue 1/2021

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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.

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Title: Cloud and edge based data analytics for privacy-preserving multi-modal engagement monitoring in the classroom
Authors: Davy Preuveneers
Giuseppe Garofalo
Wouter Joosen
Publication date: 25-02-2020
Publisher: Springer US
Published in: Information Systems Frontiers / Issue 1/2021
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-020-09993-4

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