Exploration of Intelligent Teaching Methods for Ideological and Political Education in Colleges and Universities under the Background of "Mass Entrepreneurship and Innovation". (17th August 2022)

• Record Type: Journal Article
• Authors: An, Kaiqiang
• Other Names: Zhang Yuwei Academic Editor.
• Is Part Of: International journal of antennas and propagation. Volume 2022(2022)
• Publication Date: 2022-08-17
• Subjects: Electronic apparatus and appliances -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Antennes (Électronique); Ondes radioélectriques -- Propagation; Antennas (Electronics); Electronic apparatus and appliances; Radio wave propagation; Electronic journals; Periodicals; 621.382405
• DOI: 10.1155/2022/2294908
• Languages: English
• ISSNs: 1687-5869

Abstract

Abstract : The core subject matter of the development of innovative and entrepreneurial talents in higher vocational colleges, as well as the solution to the social employment issue, is the investigation of intelligent teaching methods for ideological and political education in colleges and universities against the backdrop of "mass entrepreneurship and innovation." Artificial intelligence presents challenges of lack of emotion in the process of ideological and political education innovation in colleges and universities under the background of AI. AI does not provide information resources, technology, and thinking opportunities for the innovation of ideological and political education in colleges and universities. Therefore, this research presents a facial expression recognition approach based on facial recognition technology to address the emotional problem in intelligent teaching methods. This method can effectively and accurately identify the facial expressions of students during learning so that intelligent tools can identify students' emotions in time, make corresponding adjustments quickly, and improve teaching efficiency. According to this study's experimental findings, the facial expression recognition approach based on the upgraded AlexNet achieves an average recognition accuracy of around 75%, while the fine-tuning method based on the VGG-Face model achieves an average recognition accuracy of about 88.5%. The method based on the VGG-Face model is better suitable for face recognition in intelligent education, which can determine the status of students in real-time and alter the lesson plan, as seen by the facial expression recognition accuracy rate based on the enhanced AlexNet being 13% higher.