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

Personalisation of the multimedia content delivered to mobile device users

People using mobile devices for studying multimedia based educational content are often on the move and thus rely solely on their device battery power supply. When battery power runs low, they have to stop their activities, significantly reducing their learning outcomes and their satisfaction. This paper proposes a solution to perform the personalisation of the multimedia educational content, based both on the learner profile and on the available power resources on the device used. The solution aims to increase the battery life without affecting learner's quality of experience. Experimental results show that the battery life can be increased by changing streaming related parameters while preliminary subjective tests have assessed their impact on end user perceived quality of the multimedia clip

Pedagogical based Learner Model Characteristics

International audienceThe personalisation and adaptation of content creation, distribution and presentation aim to increase learner quality of experience, improve the learning process and increase the learning outcomes. This paper introduces a novel Learner Model that is integrated by the NEWTON project into the NEWTELP learning platform in order to support personalisation and adaptation. The NEWTON's Learner Model includes a multitude of learner characteristics, including pedagogical, disability, affective and multi-sensorial

Energy-aware Adaptive Multimedia for Game-based e-learning

Thanks to their motivational potential, video games have started to be increasingly used for e-learning. However, as e-learning gradually shifts towards mobile learning, there is a growing need for innovative techniques to deliver rich learning material such as educational games to resource-constrained devices. In particular, the limited battery capacity of mobile devices stands out as a key issue that can significantly limit players' access to educational games. This paper proposes an Energy-aware Adaptive Multimedia Game-based E-learning (EAMGBL) framework that aims to enable energy-efficient educational games delivery to mobile devices over wireless networks. The framework builds on top of the idea to render the game on the server side and stream a recording of it to the player's device over the Internet. To reduce the mobile device energy consumption and enable the player to play for longer, the proposed framework proposes to adapt both the educational game elements as well as the game's recorded multimedia stream

MediaMTool: Multimedia content management tool

With the proliferation of mobile devices, multimedia streaming over wireless networks has increased in popularity. To overcome a number of challenges as well as to enhance mobile users' experience, much research effort has been placed into multimedia content adaptation and personalisation. Supporting adaptive multimedia can pose itself a number of challenges, especially when considering the fast growing rate at which multimedia content is being produced. This paper explores the idea of automatic multimedia content management and authoring to support adaptive multimedia delivery to mobile devices. A multimedia content management tool (MediaMTool) is presented which automatically creates multiple versions of the multimedia clips based on a set of specified multimedia clip features. For testing purposes, MediaMTool was used in conjunction with EcoLearn, a m-learning system that adapts the quality of the educational multimedia clips in order to save battery power on the learner mobile device

Organizaciones matemáticas y didácticas en torno al objeto de "límite de función" : una propuesta metodológica para el análisis

Our research falls within the general field of the analysis of the teacher's activity and focuses on the specific case of teaching the concept of "limit to the function" in the Spanish secondary school system. Using the anthropological focus of didactics (Chevallard, 1998) as a general theoretical frame, we propose an investigative methodology for the analysis of mathematical organizations recreated by the teacher in the classroom in collaboration with his/her pupils and the respective didactic organizations that allow their reconstruction

User-centered EEG-based multimedia quality assessment

Multimedia users are becoming increasingly quality-aware as the technological advances make ubiquitous the creation and delivery of high-definition multimedia content. While much research work has been conducted on multimedia quality assessment, most of the existing solutions come with their own limitations, with particular solutions being more suitable to assess particular aspects related to user's Quality of Experience (QoE). In this context, there is an increasing need for innovative solutions to assess user's QoE with multimedia services. This paper proposes the QoE-EEG-Analyser that provides a solution to automatically assess and quantify the impact of various factors contributing to user's QoE with multimedia services. The proposed approach makes use of participant's frustration level measured with a consumer-grade EEG system, the Emotiv EPOC. The main advantage of QoE-EEG-Analyser is that it enables continuous assessment of various QoE factors over the entire testing duration, in a non-invasive way, without requiring the user to provide input about his perceived visual quality. Preliminary subjective results have shown that frustration can indicate user's perceived QoE

Personalised Multimedia Educational Content for M-learning Environments.

Thanks to the latest technological advances of mobile devices and web technologies, mobile learning (m-learning) has started to be adopted by an increasing community as an educational platform. There are several challenges in m-learning due to the high variety of mobile devices with different characteristics, different user profiles and various and variable network types and conditions, including the need to provide content suitable to user expectations. Solutions focus on adapting the educational material to suit user interests, goals and expectations, particularities of different user devices, or existing network conditions. As multimedia content usage in m learning has seen an exponential growth in the recent years, and as delivering multimedia content to learners is a high resource intensive task, adaptation of multimedia based educational content has become a very interesting research topic. Very few researchers in adaptive mlearning have addressed multimedia content adaptation based on mobile device characteristics or network connectivity, and to the knowledge of the author, none has studied the impact of video quality on the m-learning process. The latest is of much importance as most mechanisms for video quality adaptation involve content quality decrease. In this context, the research presented in this thesis, complements current research on adaptive mechanisms for multimedia educational content delivery with applicability in m-learning. The thesis proposes a strategy for grouping mobile learning devices in classes with similar characteristics. Each class was associated to a video profile meant to support an optimum level of quality on the National College of Ireland target devices. This research also presents a study conducted on a significant number of educational multimedia clips which makes recommendations in terms of optimum quality levels for each of the proposed video profiles. Experiments with different types of educational clips were conducted in order to determine how much the quality of the proposed video profiles can be decreased, while still maintaining good user perceived quality level. Results from a subjective study conducted on a number of participants, have validated the results from the experimental studies, and have confirmed that the learners ability to acquire knowledge is not impacted by a controlled decrease of the video quality

Feature Selection for Machine Learning-based Phishing Websites Detection

Phishing is a social engineering technique that is commonly used to deceive users in an attempt to obtain sensitive information such as username, passwords or credit card details. While there was extensive research on machine learning-based phishing detection, some prior works proposed a large number of features and not all of them are feasible to extract for real-time detection. This work combined two datasets with 30 and 48 features respectively, to identify 18 common features. Moreover, feature selection was conducted to identify 13 optimal features for a more robust model. A comparison with prior research works on the same datasets showed that the best models built on all features using the random forest algorithm scored lower on the 30 feature dataset, and achieved better performance on the 48 features dataset. The best model on the 13 features achieved an accuracy of 0.937

QoE-aware video resolution thresholds computation for adaptive multimedia

Multimedia streaming to mobile devices is one of the main sources of network congestion. As bandwidth requirements are continually increasing and users are becoming more quality-aware, there is a growing need for QoE-aware multimedia adaptation solutions. This paper presents a novel mechanism named ResCompute, which enables to automatically compute threshold values up to which the video resolution can be decreased while still maintaining a predefined QoE level. The mechanism combines full-reference objective VQA metrics and rules for mapping their values to the subjective MOS scale. The results from a subjective study with 60 participants show that mapping rules for full-reference VQA metrics such as PSNR, SSIM and VIFp provide up to 72.22% MOS level match accuracy across different categories of multimedia clips. Moreover, accurate resolution threshold values computation requires careful selection of the VQA metrics mapping rules to balance the under and overestimation of subjective video quality