Research on Recommendation of Ideological and Political Teaching Resources using Dunn Index based K-Means Clustering Approach

The dependability and efficiency of teaching results are based on a precise estimation of Ideological and Political education in colleges. This research introduces an online education approach based on clustering methods to support teacher's better understanding of the students and to recommend the different Ideological and Political courses. Thus, this research aims to optimize the teaching mechanisms of Ideological and Political courses as well as recommend them by using the Dunn Index based K-Means Clustering (DI-KMC) approach. The estimation metrices are chosen by the experiments and appropriate datasets are utilizing for the simulation as well as testing. The experimental results demonstrates that the proposed DI-KMC approach reaches a minimum Mean Square Error (MSE) of 1.116 and Mean Absolute Error (MAE) of 0.543 respectively as compared to the traditional KMC approach. These discoveries provide appropriate insights for teachers and students to improve curriculum delivery, monitor student results, and solve educational disparities efficiently. This research is of most importance for enhancing the teacher's advanced Ideological and Political approaches and enhance the management levels in online teaching.