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

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

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

User QoE assessment on mobile devices for natural and non-natural multimedia clips

Quality of Experience (QoE) has become an increasing topic of research with the proliferation of multimedia services on mobile devices. Previous research studies have focused on proposing objective video quality assessment (VQA) metrics, and evaluating them on generic video content databases that consist mainly of natural clips such as news, sports and movies. This paper investigates the accuracy of VQA metrics to estimate user's QoE for natural and non-natural multimedia clips on mobile devices. The results from a subjective study with 60 participants have shown that well known full-reference VQA metrics such as PSNR, SSIM and VIFp exhibit up to 97% QoE estimation accuracy for non-natural clips, despite not being traditionally recommended for such clips

Subjective assessment of BitDetect — A mechanism for energy-aware multimedia content adaptation

As mobile devices are becoming more compact and powerful and as they start to be increasingly used for accessing power-hungry multimedia streaming applications, there is an increasing need for mechanisms to efficiently manage the limited battery power resources. This is especially important as the battery capacity has not kept up with the power requirements of an increasing number of mobile device features and “always on” connected users. Adaptive multimedia-based power-saving mechanisms often decrease the clip bitrate to increase the mobile device battery life, without considering the effect of these degradations on the user-perceived quality. This paper proposes BitDetect, a mechanism that uses objective video quality assessment metrics to detect content-specific video bitrate levels that enable saving battery power while maintaining good user perceived quality. Results from a subjective study indicate that the recommended bitrate offers good user-perceived quality across different multimedia clips. Furthermore, experimental tests indicate that significant battery power can be saved by using the recommended bitrates when streaming multimedia clips to a mobile device