Editorial Technologies for Data-Driven Interventions in Smart Learning Environments

Abstract

Smart Learning environments (SLEs) are defined [1] as learning ecologies where students engage in learning activities, or where teachers facilitate such activities with the support of tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners and educators’ actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out. Sources from these data may include learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wide range of automatic, human-mediated, or hybrid interventions, which involve learners and teachers in the decision making behind the interventions.