QoE Measurement in E-learning Systems Based on a Videoconferencing Platform

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.