An approach to pervasive monitoring in dynamic learning contexts : data sensing, communication support and awareness provision

It is within the capabilities of current technology to support the emerging learning paradigms. These paradigms suggest that today’s learning activities and environments are pervas ive and require a higher level of dynamism than the traditional learning contexts. Therefore, we have to rethink our approach to learning and use technology not only as a digital information support, but also as an instrument to reinforce knowledge, foster collaboration, promote creativity and provide richer learning experiences. Particularly, this thesis was motivated by the rapidly growing number of smartphone users and the fact that these devices are increasingly becoming more and more resource-rich, in terms of their communication and sensing technologies, display capabilities battery autonomy, etc. Hence, this dissertation benefits from the ubiquity and development of mobile technology, aiming to bridge the gap between the challenges posed by modern learning requirements and the capabilities of current technology. The sensors embedded in smartphones can be used to capture diverse behavioural and social aspects of the users. For example, using microphone and Bluetooth is possible to identify conversation patterns, discover users in proximity and detect face-to-face meetings. This fact opens up exciting possibilities to monitor the behaviour of the user and to provide meaningful feedback. This feedback offers useful information that can help people be aware of and reflect on their behaviour and its effects, and take the necessary actions to improve them. Consequently, we propose a pervasive monitoring system that take advantage of the capabilities of modern smartphones, us ing them to s upport the awarenes s provis ion about as pects of the activities that take place in today’s pervas ive learning environments. This pervasive monitoring system provides (i) an autonomous sensing platform to capture complex information about processes and interactions that take place across multiple learning environments, (ii) an on-demand and s elf-m anaged communication infras tructure, and (ii) a dis play facility to provide “awarenes s inform ation” to the s tudents and/or lecturers. For the proposed system, we followed a research approach that have three main components. First, the description of a generalized framework for pervasive sensing that enables collaborative sensing interactions between smartphones and other types of devices. By allowing complex data capture interactions with diverse remote sensors, devices and data sources, this framework allows to improve the information quality while saving energy in the local device. Second, the evaluation, through a real-world deployment, of the suitability of ad hoc networks to support the diverse communication processes required for pervasive monitoring. This component also includes a method to improve the scalability and reduce the costs of these networks. Third, the design of two awareness mechanisms to allow flexible provision of information in dynamic and heterogeneous learning contexts. These mechanisms rely on the use of smartphones as adaptable devices that can be used directly as awareness displays or as communication bridges to enable interaction with other remote displays available in the environment. Diverse aspects of the proposed system were evaluated through a number of simulations, real-world experiments, user studies and prototype evaluations. The experimental evaluation of the data capture and communication aspects of the system provided empirical evidence of the usefulness and suitability of the proposed approach to support the development of pervasive monitoring solutions. In addition, the proof-of-concept deployments of the proposed awareness mechanisms, performed in both laboratory and real-world learning environments, provided quantitative and qualitative indicators that such mechanisms improve the quality of the awareness information and the user experience