Quality Evaluation and Satisfaction Analysis of Online Learning of College Students Based on Artificial Intelligence

Author:

Yun Shangzi12ORCID,Bai Yongfeng3ORCID,Jongnam Baek2

Affiliation:

1. Office of Labor Union, Handan University, Hebei, Handan 056005, China

2. Department of Education, Woosuk University, Korea, Jeonju 565701, Republic of Korea

3. Office of Academic Research, Handan University, Hebei, Handan 056005, China

Abstract

In order to better study the quality and satisfaction of online learning of college students, this paper analyzes and researches online learning of college students based on relevant theories of artificial intelligence. Through the traditional machine learning method to evaluate the quality of online learning, the deep learning theory is applied to the satisfaction analysis of college students'’ online learning. The results show that different statistical indexes have different influences on traditional machine learning, but they all show a gradually decreasing trend. The main reason for the different degrees of influence is that the emphasis of different statistical indexes is different, and the order from large to small is MAE > RMSE > MAPE > TIC. Statistical indicators can better describe the first stage of test data, while the corresponding quality indicators can better characterize the second stage of test data. It indicates that statistical and quality indexes should be considered comprehensively to analyze the test data accurately. The increase of evaluation indexes based on traditional machine learning can improve the evaluation indexes of online learning quality of college students. And the improvement of statistical indicators and evaluation factors can promote the accuracy of online learning quality evaluation of college students. Based on the theory of artificial intelligence, the quality and satisfaction of online learning of college students are analyzed and evaluated by using the traditional machine learning method and deep learning method, respectively. Relevant research can provide a research basis for artificial intelligence in online learning methods of college students.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Reference25 articles.

1. Optimal speed control and regulation of salient Pole hydro turbine generators in Nigeria: artificial intelligence approach;N. B. Ngang;Open Access Library Journal,2022

2. Turrin michela Optimising high-rise buildings for self-sufficiency in energy consumption and food production using artificial intelligence: case of europoint complex in rotterdam;E. Berk;Energies,2022

3. A review of hydrogen-based hybrid renewable energy systems: simulation and optimization with artificial intelligence;W. Cai;Journal of Physics: Conference Series,2022

4. Artificial Intelligence-Controlled Microfluidic Device for Fluid Automation and Bubble Removal of Immunoassay Operated by a Smartphone

5. Toward Autonomous Detection of Anomalous GNSS Data Via Applied Unsupervised Artificial Intelligence

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