Abstract-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.Index Terms-Quality of Experience, multimedia quality assessment, subjective assessment methods, objective quality metrics, EEG.

The reliable estimation of video quality has become increasingly important with the proliferation of online video services and users becoming more quality aware. A multitude of objective video quality assessment (VQA) metrics with various performance and complexity have been proposed. However, their applicability in real-world scenarios is limited by the lack of clear interpretations of how the metric values reflect the subjective user-perceived video quality. This paper proposes a novel mechanism called VQAMap, that uses data from public VQA databases and enables to automatically create generic rules for mapping the values of objective VQA metrics to the subjective MOS scale (i.e., 1 -bad, 2 -poor, 3 -fair, 4 -good, and 5 -excellent). An extensive evaluation study of VQAMap was conducted using data from three public VQA databases, considering six objective VQA metrics. The results analysis has shown that VQAMap provides mapping rules with quality estimation accuracy as high as 95%, while the variation in performance being caused by the varying accuracy of the different objective metrics.Index Terms-Video quality assessment (VQA), mean opinion score (MOS), subjective methods, objective metrics, video quality mapping, performance evaluation, public video quality databases.

Laboratory experience is a key factor in technical and scientific education, but traditional laboratories are costly to maintain, limiting possibilities for practical exercises. Virtual laboratories have been proposed to reduce cost and simplify maintenance of lab facilities, while offering students a safe environment to build up experience and enthusiasm for STEM (Science, Technologies, Engineering and Maths) subjects without geographical limitations. Virtual labs enable students to participate and interact in inquiry-based classes where they can implement and analyse their own experiments, learn by using virtual objects and apparatus. Utilising virtual labs provides students with the chance to develop critical thinking, innovative and team working skills, all of which are highly valued in today's job market. Numerous virtual labs have been developed by different organisations and large-scale international projects, and many of these are available as open source software. NEWTON project, as a part of European Union's Horizon 2020 Research and Innovation programme, is currently developing virtual labs to revolutionise the way STEM subjects are taught throughout European schools and colleges. NEWTON draws from the expertise of 14 European academic and industry partners and incorporates virtual reality with gamification and augmented reality with personalised learning. NEWTON has a special emphasis on developing virtual labs that are tailored to the needs of disabled students, such as deaf students and those with upper-limb disabilities. Here, we give a review of current virtual labs and discuss how NEWTON can overcome the existing limitations.

No abstract

No abstract

The Atomic Structure virtual lab was employed in a small-scale pilot carried out in a school for students with special education needs (SEN) from Ireland, as part of the European Horizon 2020 Project NEWTON. Atomic Structure virtual lab places the learner in the centre of the learning experience through implementation of personalisation, inquiry-based learning, and self-directed learning. These pedagogies, combined with interactive activities and the use of multimedia, make Atomic Structure an engaging, encouraging, and fun learning environment. In the lab students are active participants, not passive listeners: they are in charge of their own learning. The Atom Structure virtual lab teaches concepts regarding atoms, isotopes and molecules. Learners can also interact with atoms, build an atom from information available in the periodic table, build isotopes and molecules. Students with hearing impairment from two secondary school classes participated in the pilot. A number of surveys and questionnaires were used in the evaluation methodology including demographic questionnaire and Torrance Tests of Creative Thinking (TTCT) questionnaire. The evaluation results showed that the students' creative thinking has improved significantly in terms of various mental characteristics such as fluidity, flexibility and originality.

Engagement influences participation, progression and retention in gamebased e-learning (GBeL). Therefore, GBeL systems should engage the players in order to support them to maximize their learning outcomes, and provide the players with adequate feedback to maintain their motivation. Innovative engagement monitoring solutions based on players' behaviour are needed to enable engagement monitoring in a non-disturbing way, without interrupting the game-play and game flow. Furthermore, generic metrics and automatic mechanisms for their engagement monitoring and modelling are needed. One important metric that was used for engagement modelling is TimeOnTask, which represents the duration of time required by the player to complete a task. This paper proposes ToTCompute (TimeOnTask Threshold Computation), a novel mechanism that automatically computes-in a task-dependent manner-TimeOnTask threshold values after which student engagement decreases with a given percentage from his initial level of engagement (e.g., after 2 min student engagement will fall with 10 % from his initial level). In this way the mechanism enables engagement modelling at a higher granularity and further enables engagement-based adaptation in GBeL systems. ToTCompute makes use of game-playing information and EEG signals collected through an initial testing session. The results of an experimental case study have shown that ToTCompute can be used to automatically compute threshold values for the TimeOnTask generic engagement metric, which explains up to 76.2 % of the variance in engagement change. Furthermore, the results confirmed the usefulness of the mechanism as the TimeOnTask threshold value is highly task-dependent, and setting its value manually for multiple game tasks would be a laborious process.

No abstract