Analysis of the teaching quality on deep learning-based innovative ideological political education platform

• Published in: Progress in artificial intelligence Vol. 12; no. 2; pp. 175 - 186
• Main Authors: Yun, Gao, Ravi, Renjith V., Jumani, Awais Khan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Computer Imaging; Computer Science; Control; Data Mining and Knowledge Discovery; Deep learning; Education; Ideology; Mechatronics; Natural Language Processing (NLP); Neural networks; Pattern Recognition and Graphics; Performance evaluation; Political factors; Public policy; Radial basis function; Regular Paper; Robotics; Special Issue on Smart Technologies for Blended Learning; Vision; Deep learning; Ideological political; Innovative; Platform; Education; Teaching
• ISSN: 2192-6352; 2192-6360
• DOI: 10.1007/s13748-021-00272-0

Education ideology refers to believers, traditions, cultures and principles that govern education in economics, politics, morals, faith, knowledge and reality, aesthetics and artistic affairs. Different types of expenditure, protection against political risks, changes in government policies and trade ties are considered important factor in ideological and political education. In the paper, Deep Learning-Based Innovative Ideological Political Education Platform has been proposed to improve limitations on the functioning or restraint of multinational firms' access to teaching quality funding and trading. Information supervision quality analysis is introduced to reduce social threat perception by proper strategic assessment and implementation. Realization ideological education technique is implemented corporations use economic ties to address domestic problems. Based on experiment results, the educational activities and teaching became more effective, an educational intervention scheme is explained, and the plasticity of the brain optimized and ideological and political education performance is increased. The evaluation results of deep learning Radial Basis Function Neural Network in Innovative Ideological Political Education platform has been proposed to improve the teaching quality are calculating the mean percentage error ratio is 88.87%, reducing the political risk in teaching ratio is 83.86%, the evaluation students ratio is 76.80%, the predicting teaching error ratio is 79.13%, and the overall performance of the teaching quality is 86.55%.