PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 9, 2026International Journal of Innovation and Sustainable Development0 citationsOpen Access

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

View Full Paper
CZChenyu ZhangUniversity of Science and Technology LiaoningXZXin ZhangGuangxi UniversityJGJinming Gu

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.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69fed0abb9154b0b82877badhttps://doi.org/10.1504/ijisd.2026.10078226
Ask AI
Helpful
Bookmark
Share
View Full Paper