QoE Measurement in E-learning Systems Based on a Videoconferencing Platform
Journal
International Journal of Research and Reviews in Next Generation Networks (IJRRNGN)
Date Issued
2012-05-01
Author(s)
Malinovski, Toni
Vasileva-Stojanovska, Tatjana
Trajkovik, Vladimir
Abstract
E-learning educational systems are constantly being evaluated in a terms of performance, availability and their
capacity to meet the end-users needs at the end. Technical behavior of the equipment, infrastructure-related parameters, overall
QoS performance are lacking sufficient information about the end-users’ experience. This paper explores videoconferencing
implementation of an e-learning system, considering both necessary QoS controls (of the underlying videoconferencing
infrastructure) and students’ QoE perception (of the achieved learning). We propose basic and extended QoE measurement
methods. In the basic model, we combine two different techniques based on surveys and cognitive interviews for students’
evaluation in order to decrease the measurements errors and provide proper results. The extended approach based on ANFIS
neuro-fuzzy model, is used to identify the causal relationship between input parameters of both objective and subjective nature,
and the resulting QoE. The extended model uses the QoE estimation from the basic model as a subjective input variable to the
system. To evaluate results of the basic model and support our claims, test experiment is conducted with videoconferencing
application in two combined learning sessions, while gathered information is process through the proposed basic QoE
measurement model.
capacity to meet the end-users needs at the end. Technical behavior of the equipment, infrastructure-related parameters, overall
QoS performance are lacking sufficient information about the end-users’ experience. This paper explores videoconferencing
implementation of an e-learning system, considering both necessary QoS controls (of the underlying videoconferencing
infrastructure) and students’ QoE perception (of the achieved learning). We propose basic and extended QoE measurement
methods. In the basic model, we combine two different techniques based on surveys and cognitive interviews for students’
evaluation in order to decrease the measurements errors and provide proper results. The extended approach based on ANFIS
neuro-fuzzy model, is used to identify the causal relationship between input parameters of both objective and subjective nature,
and the resulting QoE. The extended model uses the QoE estimation from the basic model as a subjective input variable to the
system. To evaluate results of the basic model and support our claims, test experiment is conducted with videoconferencing
application in two combined learning sessions, while gathered information is process through the proposed basic QoE
measurement model.
Subjects
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