Study on fine-grained classification of MOOC ideological and political teaching resources based on an improved switching ensemble algorithm

Key Points

• This research aims to improve the classification of ideological and political teaching resources in MOOCs using advanced algorithms.
• Developed an improved switching ensemble algorithm for classification tasks.
• Conducted a series of evaluations to assess algorithm performance against traditional methods.
• Applied the algorithm to classify various MOOC teaching resources, focusing on ideological and political themes.
• The improved algorithm outperformed traditional methods with a classification accuracy increase of 15%.
• Achieved a reduction in misclassification rates for ideological resources by 20%, enhancing resource retrieval efficiency.
• Demonstrated potential for broader applications in educational resource management and categorization.