Utilization of deep learning in ideological and political education

Abstract

As society develops and educational needs continue to change, the traditional way of teaching ideology and politics is facing challenges in terms of efficiency and effectiveness evaluation. In response to the low efficiency of ideological and political education (IPE) methods and the difficulty in accurately and comprehensively evaluating students’ ideological and political literacy and moral qualities, this article used the Long Short-Term Memory with Self-Attention Mechanism (LSTM-SAM) model to conduct experiments on the evaluation of IPE effectiveness. First, by collecting information on IPE from a research center of a certain university in 2023, and then using the LSTM (Long Short-Term Memory) model to catch the long-term dependencies of IPE, the learning trajectory and changing trends of students can be better understood. The self-attention mechanism was applied to dynamically learn and distinguish the importance of different parts in the input sequence, better weighting key features such as student learning behavior and participation level, thereby enhancing the accuracy and robustness of effectiveness evaluation. Finally, the splicing method was adopted to integrate the LSTM model and self-attention mechanism for the experiment, and the teaching efficiency of different teaching methods was statistically analyzed through a questionnaire survey. The test results indicated that the classification accuracy of the LSTM-SAM model reached 98.41%, which was 1.61% higher than the LSTM model. The teaching efficiency was the highest under the gamified teaching method, providing an effective method for evaluating the effectiveness of IPE and providing useful reference for optimizing teaching methods.

Keywords

• ideological and political education
• deep learning
• teaching methods
• lstm model
• self-attention mechanism
• educational effectiveness