Analysis of the teaching quality using novel deep learning-based intelligent classroom teaching framework

Ideological is an adjective that defines theological, political and cultural views. An ideology is a bunch of ideas, and those with an ideological stand follow the main idea. The demanding characteristics of college ideological and political education include lack of research, intelligent evaluations of effective teaching quality, and an important factor. In this paper, novel deep learning-based intelligent classroom teaching framework (NDL-ICTF) has been proposed to enhance the theoretical and realistic methods and a simulation model for the network assessment of the teaching quality system at the college. The Reform Innovative Media algorithm is integrated with NDL-ICTF to set the speed and error curve for assessment measures, defines encouraging their interest in its contents, and induces them to acquire civic competences. The simulation study is based on precision, quality and results to show the durability of the system proposed. The results are estimated in NDL-ICTF as visually communicated percentage ratio is 86.16%, brain storming activities ratio is 83.86%, real-world design performance ratio is 86.55%, model creativity performance ratio is 82.55%, and foster collaboration of students ratio is 88.85% obtained from different datasets and compared with various methods.