Flipped Classroom Design of College Ideological and Political Courses Based on Long Short-Term Memory Networks

Fei Su,Zhe Fan,Shah Nazir

doi:10.1155/2021/6971906

Abstract

The advancement and rising of information technology have promoted the flipped classroom in an effective way. It flips knowledge transfer and knowledge internalization from two levels of teaching structure and teaching process, reversing the traditional teaching knowledge transfer in class and knowledge deepening after class from time and space. Although the use of flipped classrooms in ideological and political theory courses is relatively uncommon in colleges and universities, realistic teaching and related study findings in some colleges and universities provide some reference value for the use of flipped classrooms in ideological and political theory courses. As a result, the short- and long-time memory network-based flipped classroom design algorithm for ideological and political courses in colleges and universities has a wide range of applications. A neural network prediction model based on a hybrid genetic algorithm is developed in this paper. The hybrid genetic algorithm is used in this model to determine the optimal dropout probability and the number of cells in the hidden layer of the neural network. The hybrid genetic algorithm will lengthen the memory neural network to predict the teaching quality of root mean square error between real value and predictive value as a fitness function, in the process of optimization, genetic algorithm convergence to the local optimal solution of the area.

Highlights

Flipped classroom [1,2,3] benefits from the expansion of information technology. It reverses the transfer of knowledge in the classroom and the deepening of knowledge under the classroom in time and space and realizes the transfer of knowledge and knowledge from the two levels of teaching structure and teaching process
Some universities’ realistic teaching and related study findings provide some reference value for the use of flipped classrooms in ideological and political theory courses. e interpretation and perception of basic theoretical knowledge is not the only way to enhance the teaching impact of ideological and political theory courses [6, 7], but more importantly, the contradiction transformation of internalization and externalization in behavior. is is a realistic problem faced by teachers of ideological and political theory
A long short-term memory neural network [14,15,16,17] prediction model optimized by hybrid genetic algorithm is built in the design and implementation of the flipped classroom teaching process of theoretical courses and is based on neural network technology [18,19,20,21]

Summary

Introduction

Flipped classroom [1,2,3] benefits from the expansion of information technology. It reverses the transfer of knowledge in the classroom and the deepening of knowledge under the classroom in time and space and realizes the transfer of knowledge and knowledge from the two levels of teaching structure and teaching process. A long short-term memory neural network [14,15,16,17] prediction model optimized by hybrid genetic algorithm is built in the design and implementation of the flipped classroom teaching process of theoretical courses and is based on neural network technology [18,19,20,21]. It can predict and evaluate the teaching quality of flipped classrooms in college ideological and political courses. (1) To employ neural network technology to develop a long- and short-term memory neural network prediction model focused on hybrid genetic algorithm optimization that can predict and assess the teaching content of flipped college ideological and political courses (2) e root mean square error between the true value and the expected value of the teaching output predicted by the long- and short-term memory neural network is used as the fitness function in this paper to automatically find the optimal dropout probability and the number of hidden layer units of the neural network (3) To use a sequential quadratic programming algorithm to advance local search, rapidly and precisely optimize dropout probability and the number of hidden layer units, and input the obtained optimal parameters into a long- and short-term memory network to predict the teaching output of college ideological and political courses flipped classroom and evaluation

Background

Hybrid Genetic Algorithm

Experiments and Results

Method