Content-aware power saving multimedia adaptation for mobile learning

Due to the tremendous enhancements in the capabilities of mobile devices in recent years and accessibility to higher bandwidth mobile internet, the use of online multimedia learning resources on mobile devices is increasingly becoming popular. Improvements in battery capacity have not matched the same advancements compared to other features of mobile devices. Limited Battery power is introducing a significant challenge in making better use of online educational multimedia resources. Online Multimedia Resources drains more battery power as a result of higher amount of wireless data transfer and therefore limiting learning opportunities on the move. Many power saving multimedia adaptation techniques have been suggested. Majority of these techniques achieve battery efficiency while reducing multimedia quality. So far, however, to the best of our knowledge no previous effort has considered the factor of learning efficacy in multimedia adaptation process. Existing adaptation techniques are susceptible to information loss as a result of quality of reduction. Such loss affects the learning content efficacy and jeopardizes the learning process. In this paper, we recommend a novel power save educational multimedia adaptation approach that considers the learning aspect of multimedia in the adaptation process. Our technique enables learning for extended duration by battery power saving without putting the learning process at risk. Efficacy of entire learning resources is managed by not allowing any part of the learning multimedia to be delivered in a quality that will negatively affect the learning outcome. We also present a framework that guides the implementation of our approach followed by description of our prototype application that uses educational multimedia metadata implemented in semantic web technologies

Energy-Aware Streaming Multimedia Adaptation: An Educational Perspective

As mobile devices are getting more powerful and more affordable the use of online educational multimedia is also getting very prevalent. Limited battery power is nevertheless, a major restricting factor as streaming multimedia drains battery power quickly. Many battery efficient multimedia adaptation techniques have been proposed that achieve battery efficiency by lowering presentation quality of entire multimedia. Adaptation is usually done without considering any impact on the information contents of multimedia. In this paper, based on the results of an experimental study, we argue that without considering any negative impact on information contents of multimedia the adaptation may negatively impact the learning process. Some portions of the multimedia that require a higher visual quality for conveying learning information may lose their learning effectiveness in the adapted lowered quality. We report results of our experimental study that indicate that different parts of the same learning multimedia do not have same minimum acceptable quality. This strengthens the position that power-saving adaptation techniques for educational multimedia must be developed that lower the quality of multimedia based on the needs of its individual fragments for successfully conveying learning informatio

Harnessing mobile technology for classroom learning

Educational institutions are reluctant adopt mobile computing and Wireless technologies. This is because this technology remains relatively expensive compared to traditional computing technologies, mobile devices are inherently personal and can be difficult to use as a teaching tool to groups of learners, and Short Message Services and Multimedia Message Services (SMS/MMS) are expensive and limited in functional scope despite their popularity amongst young people. In this paper, we describe a component of a prototype learning environment named Quest where we propose anew way of harnessing mobile technology for learning that negates these drawbacks. In Quest we have demonstrated that the information gathering capabilities of mobile phones can be harnessed to aid learners research

MobilED – A Mobile Tools and Services Platform for Formal and Informal Learning

The MobilED initiative is aimed at designing teaching and learning environments that are meaningfully enhanced with mobile technologies and services. The MobilED deliverables are to develop a set of scenarios and guidelines of how mobile technologies could be used for teaching, learning and empowerment of students within and outside the school context; a set of concepts and prototypes that will be developed into a MobilED platform that facilitates and supports the scenarios and guidelines developed and testing, evaluation, dissemination and sustainability strategies for the MobilED platform in real contexts with real people. The first phase of the project included the design, development and piloting of a prototype platform where multimedia and language technologies (voice, text, images) are used via the mobile phone as tools in the learning process. The first 2 pilots focused on the use of low-cost mobile phones, which are readily available in the developing world. It consisted of the development of a mobile audio-wikipedia, using SMS and text-to-speech technologies to enable access to information as well as the contribution of information using voice. The application was tested and results compared between a poor, rural school environment and an affluent private school environment in South Africa. The second phase of the project looked at the use of more advanced mobile phones with multimedia capabilities. It consisted of a joint project between the “advantaged” and “disadvantaged” schools, called “Street Memory” which enabled learners to gather multimedia (sound, voice, video) information and make the results available to the community.Meraka Institute, CSIR, South Africa Media Lab, Helsinki University of Art and Design, Finlan

Thrački konjanik

Today, mobile devices have become an integral part of their possessor’s life. Learning using mobile devices has the advantage of receiving educational content at the moment when it is actually needed, or when the learner feels like s/he is able to study. Moreover, mobile devices used in education can augment the learning experience by delivering further information to the learner, depending on the learner’s context. However, there are still challenges to overcome in order to achieve a widespread adoption of mobile learning. One of these is the high cost of delivering educational content over wireless networks, especially when multimedia content is transmitted. This research proposes a solution that predicts the learner’s economic behaviour and takes it into account when delivering educational multimedia content. A mechanism for reducing the cost of the educational content delivery over wireless networks is also presented

Technology that enhances without inhibiting learning

Technology supported information sharing could be argued to both enhance and inhibit learning. However, social and affective issues that motivate learners' technology interactions are often overlooked. Are learners avoiding valuable learning applications because of privacy fears and trust issues? Will inaccurate technology assumptions and awareness inhibit information sharing? Do learners need control over technology enhanced safe creative spaces or can they be motivated to overcome badly designed systems because sharing is 'valuable' or 'fun'. This presentation details a model of privacy and trust issues that can be used to enhance elearning. Several OU case-studies of multimedia, mobile and elearning applications (conducted within IET, KMI and the Open CETL) are evaluated using this model. The model helps to identify trade-offs that learners make for technology enhanced or inhibited learning. Theories of control, identity, information sensitivity and re-use are discussed within the context of these elearning examples

Context-driven encrypted multimedia traffic classification on mobile devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.Peer reviewe

Context-driven encrypted multimedia traffic classification on mobile devices

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.Peer reviewe

A Mobile Learning Support System for Ubiquitous Learning Environments

AbstractThis paper proposes a Mobile Learning Support System (MLSS) which enables students to access learning materials by utilizing 2D barcodes and GPS technology. As the pilot system of ubiquitous learning, we used camera-equipped mobile phones and 2D barcode tags to obtain learning information from online websites. By installing the MLSS on to their mobile phones, students can scan the tag attached to the corresponding object to display related multimedia materials on the screen of mobile phones. Furthermore, MLSS also applies GPS technology to develop a location-aware environment for students. GPS technology is used to detect the students’ location and identify which 2D barcode tags are in their proximity. Therefore, this paper provides the opportunity to develop for developers create ubiquitous learning environments that combine real-world and digital world resources