Edge computing is often associated with autonomous vehicles, IoT devices, and industrial automation. But its applications are beginning to reshape digital learning environments as well. For eLearning professionals, especially those dealing with large-scale data and latency-sensitive applications like AR/VR , edge computing offers a compelling shift in how content is delivered, tracked, and personalized. In this article, we explore what edge computing is, how it differs from cloud computing, and what it means for eLearning speed, data accuracy, and learner experience. What Is Edge Computing? Edge computing is a distributed computing paradigm that brings data processing closer to the source of data generation—such as a learner's device or a local network node—instead of relying solely on centralized cloud servers. In eLearning contexts, this could mean processing a learner’s quiz results, xAPI statements, or AR/VR interactions locally before syncing with a Learning Management Sy...
Completion rates have long been a go-to metric for measuring the success of eLearning courses. While these rates can indicate participation levels, they rarely tell the full story of how effective a course truly is. To ensure learners are engaged and achieving the desired outcomes, instructional designers and eLearning professionals must explore advanced metrics. In this article, we’ll go beyond the basics and dive into advanced metrics that provide a more nuanced understanding of eLearning effectiveness. The Limitations of Completion Rates Completion rates are a surface-level metric. While they can indicate whether learners are finishing a course, they don’t answer critical questions such as: Did the learners acquire the intended knowledge or skills? Were they engaged throughout the course? How are they applying their knowledge post-training? Relying solely on completion rates can lead to a false sense of success, especially in scenarios where learners are required to complete courses...