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

Citations Over TimeTop 10% of 2024 papers

Abstract

Enhancing teaching sufficiency is crucial because low teaching efficiency has always been a widespread issue in ideological and political theory course. Evaluating data on the course is obtained from a freshmen class of 2022 using questionnaires. The data is organised and condensed for mining and analysis. Subsequently, an intelligent artificial multi-verse optimizer (AMVO) method s developed to predict the effect of ideological and political theory course. The proposed AMVO approach was tested against various cutting-edge algorithms to demonstrate its effectiveness 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 can predict the overall evaluation attitude of freshmen towards the course based on 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 further refine the evaluation of ideological and political courses by teachers and students in our school, thereby achieving the fundamental goal of moral cultivation.

Related Papers

• Three Represents: a Leap from Party's Construction Ideology to In struction Ideology(2003)
• Exploration and Practice of the Main Parties of the West in Developing Ideological Resources(2010)
• Strengthening the Ideological and Theoretical Education of Cadres(2003)
• The Historical Study of Literature as an Ideology(2011)
• Course Organisers: Course Organisers: Course Organisers: Course Organisers(2023)