Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning

Abstract

The objectives are to solve the problems existing in the current ideological and political theory courses, such as the difficulty of classroom teaching quality assessment, the confusion of teachers’ classroom process management, and the lack of objective assessment basis in teaching quality monitoring. Based on Artificial Intelligence (AI) technology, a designed evaluation method is proposed for teachers’ classroom teaching and solves some problems such as high system cost, low evaluation accuracy, and imperfect evaluation methods. Firstly, the boundary algorithm system is introduced in the research, and the Field Programmable Gate Array (FPGA) by deep learning (DL) is used to accelerate the server hardware network platform and equipped with pan tilt zoom (PTZ) and manage multiple AI + embedded visual boundary algorithm devices. Secondly, the network platform can manage the PTZ and focal length of Internet protocol (IP) cameras, measure, and capture face images, transmit data, and recognize students’ face, head, and body postures. Finally, classroom teaching is evaluated, and students’ behavioral data and functions are designed, debugged, and tested. The research results demonstrate that the method overcomes the problem of high system cost through edge computing and hardware structure, and DL technology is used to overcome the problem of low accuracy of classroom teaching evaluation. Various indicators such as attendance rate, concentration, activity, and richness of teaching links in classroom teaching are obtained. The method involved can make an objective evaluation of classroom teaching and overcome the problem of incomplete classroom teaching evaluation.

Unmasking the challenges in ideological and political education in China: A thematic review

“…In existing research, formative and outcome evaluation of courses based on deep learning technology is considered feasible and effective [ 62 ], and an education quality assessment system can be proposed [ 63 ]. Deep learning combined with algorithms such as edge computing can assess the quality of teaching [ 64 , 65 ], combining psychology and online teaching models can improve efficiency [ 66 ], the development of solid management systems using deep learning neural networks can help to improve the effectiveness of ideological and political education evaluation [ 67 ], and the Internet of things Networks and deep learning are also noteworthy for their combination in improving teaching efficiency and student learning outcomes [ 68 ].…”

Retracted: Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”