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.

中文翻译：

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基于长短期记忆网络的高校思政课翻转课堂设计

信息技术的进步和兴起，有效地推动了翻转课堂。它从教学结构和教学过程两个层面翻转知识转移和知识内化，从时间和空间上扭转了传统的课内知识转移和课后知识深化的教学模式。虽然翻转课堂在高校思想政治理论课中的运用还比较少见，但部分高校的现实教学和相关研究成果为翻转课堂在思想政治理论课中的运用提供了一定的参考价值。因此，基于短时长记忆网络的高校思政课翻转课堂设计算法有着广泛的应用。本文开发了一种基于混合遗传算法的神经网络预测模型。该模型采用混合遗传算法来确定神经网络隐藏层的最优dropout概率和单元数。混合遗传算法将记忆神经网络加长以预测真实值与预测值之间的均方根误差作为适应度函数的教学质量，在优化过程中，遗传算法收敛到该区域的局部最优解。该模型采用混合遗传算法来确定神经网络隐藏层的最优dropout概率和单元数。混合遗传算法将记忆神经网络加长以预测真实值与预测值之间的均方根误差作为适应度函数的教学质量，在优化过程中，遗传算法收敛到该区域的局部最优解。该模型采用混合遗传算法来确定神经网络隐藏层的最优dropout概率和单元数。混合遗传算法将记忆神经网络加长以预测真实值与预测值之间的均方根误差作为适应度函数的教学质量，在优化过程中，遗传算法收敛到该区域的局部最优解。