Artificial multi-verse optimisation for predicting the effect of ideological and political theory course

Enhancing teaching sufficiency is crucial because low teaching efficiency has always been a widespread issue in ideological and political theory course. Evaluation data on the course were obtained from a freshmen class of 2022 using questionnaires. The data were organised and condensed for mining and analysis. Subsequently, an intelligent artificial multi-verse optimiser (AMVO) method was developed to predict the effect of ideological and political theory course. The proposed AMVO approach was tested against various cutting-edge algorithms to show its efficacy and stability on the benchmark functions. The experimental results indicated that AMVO ranked first among the 23 test functions. Furthermore, the binary AMVO enhanced k-nearest neighbour classifier had excellent performance in the art ideological and political theory course in terms of error rate, accuracy, specificity and sensitivity. This model enables us to predict the overall evaluation attitude of freshmen towards the course within the dataset. In addition, we can further analyse the potential correlations between factors that enhance the intellectual and political content of the course. This model can be used to further enhance teachers' and students' evaluation of ideological and political course in our school and realise the essential objective of building morals.