Abstract

Abstract

Abstract
The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students’ level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.

Abstract

Abstract
In the last years, the amount of devices that can be connected to a network grew significantly allowing to, among other tasks, collect data about the environment or the people in it in a non-intrusive way. This generated nowadays well-known topics such as Big Data or the Internet of Things. This also opened the door to the development of novel and interesting applications. In this paper we propose a distributed system for acquiring data about the users of technological devices in a non-intrusive way. We describe how this data can be collected and transformed to produce meaningful interaction features, that reveal the state of the individuals. We analyse the requirements of such a system, namely in terms of storage and speed, and describe three prototypes currently being used in three different domains of application.

Abstract
The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students' level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.