Comparative Study of the Mobile Learning Architectures

International audienceWith the emergence of mobile devices (Smart Phone, PDA, UMPC, game consoles, etc.), learning is changing from electronic learning (e-Learning) to mobile learning (m-learning). In fact, due to the mobility feature, it seems that the m-learning have to be adapted with the change within the context. Several researches addressed this issue and implemented a mobile learning environment to prove its usefulness and feasibility in various domains. In this article, we conduct a comparative study between a list of mobile learning architectures and methods that are presented in the literature. The performance of these architectures is evaluated based on several criteria, such as the adaptation management, which is an important parameter for the management and customization of the learning resources for the learners, as well as the environment, which is a core part of mobile learning systems

The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election

The present study argues that political communication on social media is mediated by a platform's digital architecture, defined as the technical protocols that enable, constrain, and shape user behavior in a virtual space. A framework for understanding digital architectures is introduced, and four platforms (Facebook, Twitter, Instagram, and Snapchat) are compared along the typology. Using the 2016 US election as a case, interviews with three Republican digital strategists are combined with social media data to qualify the studyies theoretical claim that a platform's network structure, functionality, algorithmic filtering, and datafication model affect political campaign strategy on social media

Integrating mobile robotics and vision with undergraduate computer science

This paper describes the integration of robotics education into an undergraduate Computer Science curriculum. The proposed approach delivers mobile robotics as well as covering the closely related field of Computer Vision, and is directly linked to the research conducted at the authors’ institution. The paper describes the most relevant details of the module content and assessment strategy, paying particular attention to the practical sessions using Rovio mobile robots. The specific choices are discussed that were made with regard to the mobile platform, software libraries and lab environment. The paper also presents a detailed qualitative and quantitative analysis of student results, including the correlation between student engagement and performance, and discusses the outcomes of this experience

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Big Data Caching for Networking: Moving from Cloud to Edge

In order to cope with the relentless data tsunami in $5G$ wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware $5$G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in $5$G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of $16$ BSs with $30\%$ of content ratings and $13$ Gbyte of storage size ($78\%$ of total library size), proactive caching yields $100\%$ of users' satisfaction and offloads $98\%$ of the backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special Issue on Communications, Caching, and Computing for Content-Centric Mobile Network