Enabling Workers to Enter Industry 4.0: A Layered Mobile Learning Architecture

Manufacturing companies have to meet a lot of challenges in continuing training for employees. Especially on the way towards industry 4.0 the workforce needs to be able to handle fast changing environments and ever-changing working contexts. Furthermore, they have to be familiar with constantly new technologies (e.g. complex user interfaces, mobile devices) that are introduced during the process of company development. Due to this, working people are facing a lifelong learning process and need to evolve to knowledge workers. To fulfill these requirements new concepts are necessary for human resources development directly at the workplace and therefore adequate artifact designs. In this paper we design a layered architecture for mobile learning at the workplace. This layered approach offers the possibility to educate employees with different qualifications and skills using an integrated solution. Further, we propose to implement appropriate components at each layer to support different kinds of learning

Agente de contexto para dispositivos móveis

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 200

Energy-Aware Mobile Learning:Opportunities and Challenges

As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided

Modeling, Designing, and Implementing an Ad-hoc M-Learning Platform that Integrates Sensory Data to Support Ubiquitous Learning

Learning at any-time, at anywhere, using any mobile computing platform learning (which we refer to as “education in your palm”) empowers informal and formal education. It supports the continued creation of knowledge outside a classroom, after-school programs, community-based organizations, museums, libraries, and shopping malls with under-resourced settings. In doing so, it fosters the continued creation of a cumulative body of knowledge in informal and formal education. Anytime, anywhere, using any device computing platform learning means that students are not required to attend traditional classroom settings in order to learn. Instead, students will be able to access and share learning resources from any mobile computing platform, such as smart phones, tablets using highly dynamic mobile and wireless ad-hoc networks. There has been little research on how to facilitate the integrated use of the service description, discovery and integration resources available in mobile and wireless ad-hoc networks including description schemas and mobile learning objects, and in particular as it relates to the consistency, availability, security and privacy of spatio-temporal and trajectory information. Another challenge is finding, combining and creating suitable learning modules to handle the inherent constraints of mobile learning, resource-poor mobile devices and ad-hoc networks. The aim of this research is to design, develop and implement the cutting edge context-aware and ubiquitous self-directed learning methodologies using ad-hoc and sensor networks. The emphasis of our work is on defining an appropriate mobile learning object and the service adaptation descriptions as well as providing mechanisms for ad-hoc service discovery and developing concepts for the seamless integration of the learning objects and their contents with a particular focus on preserving data and privacy. The research involves a combination of modeling, designing, and developing a mobile learning system in the absence of a networking infrastructure that integrates sensory data to support ubiquitous learning. The system includes mechanisms to allow content exchange among the mobile ad-hoc nodes to ensure consistency and availability of information. It also provides an on-the-fly content service discovery, query request, and retrieving data from mobile nodes and sensors