Investigating quality of Experience and Learning (QoE &amp;amp; QoL) of audiovisual content broadcasting to learners over IP networks

Kalliris, George; Dimoulas, Charalampos; Veglis, Andreas; Matsiola, María

doi:10.1109/iscc.2011.5983946

Cited by 17 publications

(22 citation statements)

References 8 publications

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“…In the last few years, QoE ("Quality of Experience", 2011) has emerged as a fully user-oriented approach which focuses on the social element, users' subjective expectation and evaluation of systems' delivery and performance (Kalliris, Dimoulas, Veglis, & Matsiola, 2011;Kist & Brodie, 2012;W. Wu et al, 2009;Zhang, Xu, & Cheng, 2011).…”

Section: Purpose Of the Studymentioning

confidence: 99%

“…Having in mind the lack of literature that explores adult students' behavior, if we approach adult students the same as any distance education practitioner and reuse the variables of technology acceptance models aligned with adult learning theories, we can provide results that explain their nature and subjective experience. In the same context, different studies have already demonstrated QoE as a more holistic evaluation than mere narrowly focused end-user experience (Kalliris et al 2011;Molnar, Hava-Muntean, & Cristea, 2009;W. Wu et al, 2009;Zhang et al, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

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Adult Students' Perceptions in Distance Education Learning Environments Based on a Videoconferencing Platform – QoE Analysis

Malinovski¹,

Vasileva-Stojanovska²,

Jovevski³

et al. 2015

JITE:Research

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Distance education learning environments provide tremendous convenience and flexibility, allowing busy, mobile adult learners to engage in education while coping with their limited resources in terms of time, energy and finances. Following a student-centered approach this study investigates adult students' subjective perceptions while using distance education systems based on a videoconferencing platform as Quality of Experience (QoE). Based on a literature review, sociological behavior and expectations, we have constructed a structural equation model (SEM) illustrating relations among different variables that can predict positive levels of adult students' QoE, thus providing guidelines for proper development. We have tested the model using a survey of 198 primary education school teachers involved in a videoconferencing-based learning program for teacher enhancement. Results show a good fit to the model developed. The analysis showed that adult students' QoE is directly influenced by appropriateness of teacher-student interaction and ease of participation, as well predicted by students' motivation to attend similar trainings. Additionally, we found that variances in technical quality did not directly influence their QoE from the learning sessions.

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Section: Purpose Of the Studymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Adult Students' Perceptions in Distance Education Learning Environments Based on a Videoconferencing Platform – QoE Analysis

Malinovski¹,

Vasileva-Stojanovska²,

Jovevski³

et al. 2015

JITE:Research

View full text Add to dashboard Cite

show abstract

“…Various models have been developed for the prediction of the expected QoE (and QoL), using content attributes as inputs [2]- [6], [18]. While many works attempt to directly predict the final QoE using both Full Reference (FR) and Non Reference (NR) methods, including machine learning approaches [14]- [21], in other cases, content profiling and encoding quality classification taxonomies are employed [2]- [3]. An advantage of the last approach might be found on the fact that digitization and compression parameters can be defined prior to the selection of the content format and its final encoding /packaging, an issue that is quite useful in educational applications [3].…”

Section: Problem Definition and Background Workmentioning

confidence: 99%

“…While many works attempt to directly predict the final QoE using both Full Reference (FR) and Non Reference (NR) methods, including machine learning approaches [14]- [21], in other cases, content profiling and encoding quality classification taxonomies are employed [2]- [3]. An advantage of the last approach might be found on the fact that digitization and compression parameters can be defined prior to the selection of the content format and its final encoding /packaging, an issue that is quite useful in educational applications [3]. This also seems to be quite beneficial, considering that ground-truth libraries with already labeled content, in terms of quality profiles [2], cannot face all the demands that are encountered in various real world mediated learning scenarios.…”

Section: Problem Definition and Background Workmentioning

confidence: 99%

“…Mediated learning has also been elaborated with the use of various QoL metrics, incorporating QoE attributes of the involved Audio-Visual Educational Content (AVEC), in combination with many additional parameters that influence the overall outcome of the learning process [3], [7]- [8]. Another research topic that has recently received a lot of attention refers to the analysis of the perceived emotions caused by different audiovisual content [9]- [11], including the sentiment monitoring of all the actors (teachers and students) that are involved in mediated e-learning [8], [12]- [13].…”

Section: Introductionmentioning

confidence: 99%

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Emotional descriptors and quality of experience (QoE) metrics in evaluating mediated learning

Kotsakis

Dimoulas

Kalliris

et al. 2014

IISA 2014, the 5th International Conference on Information, Intelligence, Systems and Applications

Self Cite

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The present paper focuses on the extraction and evaluation of salient audiovisual features for the prediction of the encoding requirements in multimedia learning content. Decisions over audiovisual encoding are related to the perceived quality of experience (QoE), but also to the physical attributes of initial material (i.e. resolution, color range, motion activity, audio dynamic range, bandwidth, etc.). Recent research showed that such decisions can be really crucial during the production of audiovisual e-learning material, where poor encoding may lead to unaccepted QoE or even to the creation of negative emotional response. On the other hand, exaggerated high quality encoding may create increased bandwidth demands that are associated with annoying delays and irregular playback flow, resulting again in QoE degradation with similar emotional repulsion. Thus, there has to be a careful treatment with proper encoding balance during the production of both the networked distance learning and stand-alone audiovisual mediated resources. Such machine creativity strategies are investigated in the current work with the utilization of applicable audiovisual features, QoE metrics and emotional measures. The current work is part of a broader research, aiming at implementing intelligent models for optimal audiovisual production and encoding configuration, with respect to the source content attributes, the requested quality of experience (and learning) and the related emotional properties.

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Towards Intelligent Cross-Media Publishing: Media Practices and Technology Convergence Perspectives

Veglis

Dimoulas

Kalliris

2016

Media Convergence Handbook - Vol. 1

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Investigating quality of Experience and Learning (QoE & QoL) of audiovisual content broadcasting to learners over IP networks

Cited by 17 publications

References 8 publications

Adult Students' Perceptions in Distance Education Learning Environments Based on a Videoconferencing Platform – QoE Analysis

Adult Students' Perceptions in Distance Education Learning Environments Based on a Videoconferencing Platform – QoE Analysis

Emotional descriptors and quality of experience (QoE) metrics in evaluating mediated learning

Towards Intelligent Cross-Media Publishing: Media Practices and Technology Convergence Perspectives

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