Ioana Ghergulescu

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 users Quality of Experience (QoE). In this context, there is an increasing need for innovative solutions to assess users 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 users QoE with multimedia services. The proposed approach makes use of participants 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 users perceived QoE.

A Novel Sensor-Based Methodology for Learner's Motivation Analysis in Game-Based Learning

Learners motivation is one of the main aspects that need to be addressed for a successful learning process. Consequently, learner motivation assessment and measurement have attracted significant research interest in the e-learning area in general and game-based learning in particular. Traditional methodologies for learner motivation analysis rely on data collected through questionnaires. However, this approach does not fit well in the context of game-based learning, because an out-of-game questionnaire breaks the game users flow and immersion. This paper presents a novel electroencephalography (EEG) sensor-based methodology that supports real-time non-disturbing automatic measurement and analysis of learners motivation in game-based learning. An evaluation case study with participants playing an educational game was conducted in order to investigate the feasibility of the proposed sensor-based methodology and to compare the proposed methodology with the intrinsic motivation inventory-based questionnaire methodology. The results analysis has shown that the sensor-based methodology outperforms the traditional questionnaire-based methodology. The traditional questionnaire-based methodology is limited to analysing learners motivation on short game-playing durations, while losing its feasibility when analysing learners overall motivation over a long game-playing duration. Results have shown that learners motivation changes in time during the game-play period and the learners self-report of his/her motivation, assessed through the questionnaire-based methodology, tends to reflect only the last moments before the questionnaire is answered. Conversely, the proposed EEG sensor-based methodology is more suitable to analyse learners motivation on both short and long game-playing durations (e.g. game tasks, game levels, etc.), with the additional benefit of not interrupting the game-play and not breaking the game flow and the learners immersion with the game.

Measurement and Analysis of Learner’s Motivation in Game-Based E-Learning

Computer games started to be integrated in the learning process in order to bridge the gap between the new learner generation and the traditional learning process. However, today’s game-based e-learning environments do not provide different types of adaptation, with learners receiving mostly “one size fits all” educational games despite the existing differences between them in terms of learner knowledge, motivation, etc. In this context, game-based e-learning can lead to demotivated learners. Therefore, there is a need for adaptation strategies. In order to make adaptation possible, real-time assessment of the game-play process as well as of the learning process is needed. Since learner motivation plays an important role in both the learning and the gaming process, and can easily change, new techniques for automated assessment of learner motivation are needed. This chapter presents current trends in game-based e-learning assessment in general focusing on the assessment of learner motivation in particular. Methods for gathering information on player/learner motivation are also presented. Information on learner motivation can be gathered (1) through dialog-based interaction, (2) through game-play-based interaction and/or (3) through additional equipment. This chapter also proposes four generic metrics for the measurement and analysis of motivation in game-based e-learning based on metrics that were used in e-learning. Each metric is presented and its usage and interpretation in gaming-based e-learning are discussed.

A novel methodology for mapping objective video quality metrics to the subjective MOS scale

With the rapid growth in video-based services, and as users are becoming increasingly quality-aware, the reliable estimation of video quality has become extremely important. While a multitude of objective Video Quality Assessment (VQA) metrics with various performance and complexity have been proposed, the nonlinearity of video quality and the lack of clear interpretations of the metrics make difficult to understand how the objective metric values reflect the video quality as perceived subjectively in terms of Mean Opinion Scores (MOS). This paper proposes and evaluates a methodology for mapping objective VQA metric values to subjective MOS scores based on publicly available VQA databases. Three different databases were used for comparing the performance of various objective metrics and evaluating the proposed methodology.

VQAMap: A Novel Mechanism for Mapping Objective Video Quality Metrics to Subjective MOS Scale

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.

Motivation Monitoring and Assessment Extension for Input-Process-Outcome Game Model

This article proposes a Motivation Assessment-oriented Input-Process-Outcome Game Model (MotIPO), which extends the Input-Process-Outcome game model with game-centred and player-centred motivation assessments performed right from the beginning of the game-play. A feasibility case-study involving 67 participants playing an educational game and measuring their motivation through a questionnaire was conducted. The results have shown statistical significant difference between the motivation to play and motivation to learn, as well as statistical significant relationship between the players motivation during the game-play and the players initial motivation. A statistical significant increase in players motivation to learn about the subject presented in the game was also found. These facts confirm the usefulness of assessing players motivation from the beginning of the game-play. Furthermore, the results have shown the usefulness of the proposed model for assessing the impact of the game-play on players motivation.

Performance evaluation of EMOS model for mapping-based Video Quality estimation

Accurate user-perceived video quality estimation models are increasingly needed with the proliferation of multimedia services. Previous research studies have focused on proposing and evaluating objective Video Quality Assessment (VQA) metrics, without mapping their values to Mean Opinion Scores (MOS). This paper presents a model to compute the estimated user-perceived video quality (EMOS), by combining multiple objective VQA metrics whose continuous values are mapped to discrete scores on the 0 - 5 MOS scale. The results analysis of a subjective video quality assessment study with 60 participants have shown that combining multiple VQA metric mappings can improve the user-perceived quality estimation accuracy up to 98.5%.

Supporting Motivation Based Educational Games Through Web 3.0

Over the past decade there have been significant technological advantages that gradually brought us towards Web 3.0. Web 3.0 represents the next generation of Web that supports semantic and personalised Web. At the same time, the latest technological developments specific to today’s digital era have contributed to significant changes in the area of e-learning in general and educational games in particular. The latest Adaptive e-Learning Systems (AeLS) personalise the educational content and the learning process to better suit learner’s particular needs. However, keeping students motivated for the entire learning session represents a challenging task and therefore measurement and assessment of learner’s motivation is an important research area in the e-learning field. On another hand, due to the high success of gaming among young people, e-learning systems started to integrate games into the learning process. However, currently the educational games do not follow the same trend that sees games in general becoming more affective. Therefore, educational games are not motivational as they should be. This paper bridges research on motivation measurement and assessment from two areas of e-learning and gaming, and presents how various motivation modelling solutions applied in e-learning can be integrated with educational games. This chapter also presents how learner’s motivation can be specified through Web 3.0 using metadata.

Energy consumption analysis of cloud-based video games streaming to mobile devices

Within the growing of the gaming industry and mobile technologies, cloud-based video games streaming to mobile devices gains popularity fast. However, several issues and challenges such as user responsiveness, video quality, service quality, operating cost and energy consumption have to be addressed. Previous research studies did not thoroughly investigate the energy consumption of mobile devices used for cloud gaming. This paper focuses on how mobile device energy consumption is impacted by video game content characteristics, transmission protocol and wireless network type. The results show that game content characteristics impact energy consumption (i.e., up to 47% between games at maximum brightness for OLED screens), UDP is more energy efficient than TCP (i.e., up to 13.6%), while WiFi is more energy efficient than 3G (i.e., up to 40%). The outcome of this study can provide beneficial input for adaptive energy efficient cloud-based video games streaming mechanisms.

Large Scale Evaluation of Learning Flow

Personalized and adaptive learning is the fastest growing field in e-learning. Adaptive e-learning systems are typically well suited for real-world heterogeneous users, which exhibit different levels of motivation and knowledge. Furthermore, students learn best when they are in flow, i.e. when the level of difficulty is perfectly adjusted to their individual abilities. A personalized, adaptive, and intelligent learning environment can provide each student with this learning experience. In this paper, we present a large-scale evaluation of learning in flow within an adaptive and personalized system, the Adaptemy system. The paper presents the results of two studies: an objective study with 7,614 Irish secondary school students in math classes assessing their learning flow, and a subjective study with 80 students assessing their perceived learning experience. The results from the objective study show that 88% of the students worked within the flow channel. In the subjective study, 70% of students reported a perceived improvement in their math skills after the exercise studying with the adaptive and intelligent learning system.