Dynamic Analysis of Deep Integration of Artificial Intelligence Based on High-Performance Computing for Ideological and Political Teaching Evaluation

• Published in: Mobile information systems Vol. 2022; pp. 1 - 15
• Main Authors: Li, Ye, Yao, Dengfeng
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 18.05.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Computation; High performance computing; Ideology; Innovations; Learning; Neural networks; Random access memory; Science education; Simulation; Students; Teaching
• ISSN: 1574-017X; 1875-905X
• DOI: 10.1155/2022/4748544

At present, the teaching evaluation of courses is more and more important, and teaching evaluation is a main means to measure the quality of teachers’ teaching. Among many course teaching evaluations, ideological and political courses have become the main position for teaching evaluation reform due to their unique strategic position and subject specificity. In the era of rapid development of science and technology, artificial intelligence combined with high-performance computing has made frequent progress in various fields. The trend of development is unstoppable. Therefore, based on the current problems in the evaluation of ideological and political teaching, this paper proposes an artificial intelligence theory based on high-performance computing. This paper will closely integrate it with the evaluation of ideological and political teaching. In the research process of this paper, high-performance computing, artificial intelligence, and ideological and political teaching evaluation are explained in detail. This paper focuses on the introduction of artificial intelligence algorithms for high-performance computing. Finally, this paper proves the feasibility of this method in the evaluation of ideological and political teaching through the actual integration experiment with the evaluation of ideological and political teaching and proposes a series of teaching evaluation methods. Finally, the experiment shows that our new evaluation method can pay attention to 56.7% of students’ comprehensive development and 63.7% of students’ personal quality.