A Comparative Study on Optimizing Intelligent Tutoring System Based on Genetic Algorithm to Enhance the Effectiveness of Ideological and Political Education

As people’s demand for high-quality development of education becomes stronger and stronger, the field of education is paying more and more attention to the fundamental task of education by establishing moral values. In this paper, the improved genetic algorithm based on predation strategy is applied to the learning path recommendation system to realize the auxiliary teaching of Civics education. The study first proposes a Bayesian knowledge tracking model based on multiple interactions for knowledge tracking of students’ Civics and Political Science competence, and carries out model comparison and knowledge tracking visualization and analysis on three datasets and the real dataset of practice questions. Then according to the constructed learner model and knowledge connectivity state model, the personalized learning path construction model is designed by using learner features, knowledge point features, and generic learning paths as inputs, combined with the improved genetic algorithm based on predation strategy. The intelligent assisted teaching system designed in this paper is put into practice for Civics teaching and scored by questionnaire and paired t-test method. The results of the study said that the knowledge tracking model proposed in this paper compared with other models, the model in this paper improves the accuracy rate by 1%~2%. Using the non-elite individual set to enrich the population diversity to participate in genetic operation and iteration, the experiment shows that PSGA performs well in multiple comparisons with PSO and SGA methods, and can construct personalized learning paths more accurately, stably and effectively. The results of teaching practice show that the teaching system proposed in this paper can effectively improve students’ learning ability in Civics.