Abstract
With the popularization and application of online education in the world, how to evaluate and analyze the classroom teaching effect through scientific methods has become one of the important teaching tasks in colleges. Based on this, this paper studies the application of the GA-BP neural network algorithm. Firstly, it gives a brief overview of the current situation of online education and GA-BP neural network algorithm. Secondly, through the investigation of the online education system in many aspects, it evaluates students' online education classroom teaching quality from five aspects, and this paper proposes a more scientific online education classroom teaching quality evaluation optimization model and finally verifies the reliability of the online education teaching evaluation model through the practice in a university. The results show that the GA-BP neural network-based evaluation optimization model can effectively evaluate the online education in the process of analyzing the quality of online education classroom teaching of most professional students.
Highlights
Arciszewskl and Ziarko put forward an adaptive classroom teaching model of online education in colleges and universities based on multistrategy technology. rough the analysis of students’ personality and online education advantages, different levels of classroom teaching can be carried out for different students, and hierarchical teaching can be realized in the teaching process [6]. rough experiments, Daniel et al have proved that teaching methods can play a good role, effectively improve the effectiveness of online education classroom teaching, and use a number of indicators to evaluate students’ online education ability [7]
Based on the traditional model theory and practical experience of online education, Ma et al found that there is a problem of poor oral English in the current online education classroom teaching, so they proposed an adaptive teaching method based on the machine vision algorithm [8]. rough listening analysis of different online education dialogues, Gao et al made students reach a state of deep thinking in the process of learning [9]
Alarifi et al proposed a new group online education and teaching method based on hyperchaotic mapping, which used the transformed chaotic sequence to scramble the position of the original online method, and realized the optimal determination of various teaching methods in the process of online education classroom teaching in colleges and universities [15]. e results of Constantin et al show that the teaching scheme of group online education in colleges and universities has good teaching effect, which is suitable for the online education of junior and senior students [16]
Summary
Ziarko put forward an adaptive classroom teaching model of online education in colleges and universities based on multistrategy technology. rough the analysis of students’ personality and online education advantages, different levels of classroom teaching can be carried out for different students, and hierarchical teaching can be realized in the teaching process [6]. rough experiments, Daniel et al have proved that teaching methods can play a good role, effectively improve the effectiveness of online education classroom teaching, and use a number of indicators to evaluate students’ online education ability [7]. This paper proposes a GA-BP neural network algorithm-based online education classroom teaching quality evaluation optimization model.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
