scholarly journals Artificial Intelligence-based Learning Behavior Data Mining and Network Teaching Quality Monitoring Mechanism

2020 ◽  
Vol 1533 ◽  
pp. 032058
Author(s):  
Leilei Jiang ◽  
Ke Dong
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Teaching Quality ◽  
Quality Monitoring ◽  
Learning Behavior ◽  
Network Teaching ◽  
Monitoring Mechanism

scholarly journals Construction of the Teaching Quality Monitoring System of Physical Education Courses in Colleges and Universities Based on the Construction of Smart Campus with Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiang Huang ◽  
Xingyu Huang ◽  
Xiaoping Wang
Keyword(s):  
Artificial Intelligence ◽  
College Students ◽  
Physical Education ◽  
Monitoring System ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
Quality Monitoring ◽  
Quality Of Education ◽  
Smart Campus

With regard to the development of colleges and universities, ensuring the quality of education is the fundamental goal and main task of teaching daily management. With the continuous improvement of the application level of the Internet and other information technologies, the construction of smart campus in colleges and universities in China is rapidly advancing. This paper studies the construction and innovation strategy of the public sports quality monitoring system and discusses the changes in college students’ sports quality after the introduction of smart campuses from the perspective of artificial intelligence and the creation of smart universities. In this paper, the field survey method and other research methods are combined to study, and in the process of data storage, SQL Server database platform is used to store the data. This study shows that the proportion of each element of physical state management has changed significantly before and after college entrance. According to the data, since the introduction of smart campus real name system identification, tracking data, and evaluation functions, the number of college students’ physical exercise has increased significantly. The number of students with exercise plan in school 1 has increased from 70 to 222, and that of school 2 has increased from 49 to 199. Before the introduction, the students were very satisfied with the learning effect of physical education, which was 40.12% and increased to 45.70% after the introduction. Before the introduction, the students were very satisfied with the sports equipment, which was 30.12% before the introduction and increased to 35.24% after the introduction. Therefore, building a system for monitoring the quality of public sports in universities is very important for improving the quality of education in public sports in universities and plays an active role in promoting the physical and mental health of students.

Application of Web Data Mining in On-Line Education

2013 ◽  
Vol 684 ◽  
pp. 526-530
Author(s):  
Lin Na Huang ◽  
Guo Xiang Liu
Keyword(s):  
Data Mining ◽  
Teaching Method ◽  
Teaching Quality ◽  
Web Data ◽  
Web Data Mining ◽  
Mining Technology ◽  
Network Teaching ◽  
On Line ◽  
Resource Library

On-line education, as a new teaching method, introduces Web data mining into on-line education to develop intelligentized and individual construction of resource library and on-line education. Web data mining technology can help to find out education laws and modes to meet different students’ individuation, reaching Network level teaching and improving Network teaching quality. This paper analyses problems existed in current on-line education by pointing out necessary Web data mining technology and its application in on-line education.

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

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