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A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data

Chen Tan¹, Jianzhong Lin¹

¹Shanghai Jiao Tong University, Shanghai, 200040 China.

Soft Computing

|June 15, 2021

Summary

This study introduces a new data mining model to predict the quality of experience (QoE) in virtual education systems. The model accurately identifies key factors influencing student engagement and learning outcomes in online environments.

Keywords:

Association rules mining Classification Data mining E-learning Virtual education system

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Area of Science:

Educational Technology
Computer Science
Data Mining

Background:

Emerging technologies like 5G, IoT, and cloud-edge computing are transforming higher education.
The COVID-19 pandemic accelerated the adoption of online teaching and e-learning systems.
Predicting the quality of experience (QoE) in virtual education is crucial due to the shift in educational delivery.

Purpose of the Study:

To develop a novel prediction model for assessing QoE in virtual education systems.
To identify and analyze the technical and behavioral factors impacting teaching and learning in online environments.
To leverage data mining techniques for predicting student performance and engagement in e-learning.

Main Methods:

Utilized data mining techniques, including association rules mining and supervised learning.
Developed a new prediction model to detect technical aspects of virtual education systems.
Applied the model to analyze behavioral aspects of teaching and learning.

Main Results:

The proposed prediction model demonstrated high accuracy, precision, and recall.
Effectively identified efficient QoE factors within virtual education systems.
The model accurately predicts the behavioral aspects of teaching and e-learning for students.

Conclusions:

The developed data mining model offers a reliable approach for predicting QoE in virtual education.
Findings highlight the importance of technical and behavioral factors in optimizing online learning experiences.
This research contributes to improving the effectiveness and quality of e-learning systems.