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.

学习管理系统是支持培训计划和教育课程的管理和交付的服务平台。可以在混合，翻转或完全在线教室中提供预先录制的实时或交互式讲座。这种服务平台的主要挑战是对参与度的充分监控，因为这是学生学习成绩的早期指标。的确，观察观众的行为并保持参与者的参与不仅是面对面的环境中的挑战，在这种面对面的环境中学生和教师共享相同的体育学习环境，而且绝对是当学生远程参与时。在这项工作中，我们提出了一种用于多模式参与度分析的混合云和基于边缘的服务编排框架。我们实现并评估了基于边缘的浏览器解决方案，用于通过安全的多方计算跨用户聚合来分析不同的行为方式。与现代在线学习系统相比，我们基于云边缘的混合解决方案的优势是双重的。随着越来越多的学生扩大规模，它还减轻了在线学习中分析的兴起引发有关负责任地使用数据的问题的时代的隐私问题。