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<p>Ideological and political education emphasizes the cultivation of moral values, critical thinking, and a sense of social responsibility, and traditionally relies on static teaching. Due to the development of the digital environment, there is a need to shift towards more flexible, dynamic, and attractive models. The integration of software technology into ideological and political education is a modern educational reform method that combines technology with the cultivation of values. Research and collect various types of data to improve the effectiveness of ideological and political education, and use data preprocessing techniques to ensure data quality. Research discovers the potential of combining Cheetah Optimized Efficient Recurrent Neural Networks (CO-ERNN) with ideological education methodologies to generate a dynamic, personalized, and adaptive learning environment. The proposed model enhances the power of ERNNs, which are further enhanced by CO algorithms, to handle large and complex datasets. This is achieved by incorporating learner responses to continuously improve and adapt the educational process, promoting critical thinking, reasoning, and cultural awareness. Initial findings highlight the effectiveness of this method in creating a personalized and adaptive learning environment that increases students’ motivation and retention of important concepts. This model then helps foster critical thinking, reasoning, and cultural awareness through the guided delivery of content, as determined by the learner’s interactions. By outperforming LSTM-SAM, GRU-Attention and CNN-LSTM (98.85%), precision (99.05%), and recall (98.55%), the CO-ERNN model demonstrates how well it can adapt and retrieve instructional data for better learning results and MAE (0.652), RMSE (0.418), resulting in less error values in the model of CO-ERNN. This innovative educational model holds promising potential for reshaping the delivery and impact of ideological education in the digital age, though findings are still preliminary and based on a limited dataset.</p>

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Software technology helps ideological education form a new educational paradigm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Xiaoqin Ju,
  • Hong Xiang

摘要

Ideological and political education emphasizes the cultivation of moral values, critical thinking, and a sense of social responsibility, and traditionally relies on static teaching. Due to the development of the digital environment, there is a need to shift towards more flexible, dynamic, and attractive models. The integration of software technology into ideological and political education is a modern educational reform method that combines technology with the cultivation of values. Research and collect various types of data to improve the effectiveness of ideological and political education, and use data preprocessing techniques to ensure data quality. Research discovers the potential of combining Cheetah Optimized Efficient Recurrent Neural Networks (CO-ERNN) with ideological education methodologies to generate a dynamic, personalized, and adaptive learning environment. The proposed model enhances the power of ERNNs, which are further enhanced by CO algorithms, to handle large and complex datasets. This is achieved by incorporating learner responses to continuously improve and adapt the educational process, promoting critical thinking, reasoning, and cultural awareness. Initial findings highlight the effectiveness of this method in creating a personalized and adaptive learning environment that increases students’ motivation and retention of important concepts. This model then helps foster critical thinking, reasoning, and cultural awareness through the guided delivery of content, as determined by the learner’s interactions. By outperforming LSTM-SAM, GRU-Attention and CNN-LSTM (98.85%), precision (99.05%), and recall (98.55%), the CO-ERNN model demonstrates how well it can adapt and retrieve instructional data for better learning results and MAE (0.652), RMSE (0.418), resulting in less error values in the model of CO-ERNN. This innovative educational model holds promising potential for reshaping the delivery and impact of ideological education in the digital age, though findings are still preliminary and based on a limited dataset.