Article: Study on multimodal ideological and political teaching material push on MOOC online learning platform Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.8 pp.55 - 74 Abstract: The expected goal is to address the issues of low coverage, high latency, and high average absolute error in traditional push methods for ideological and political teaching resources. This study focuses on the multimodal ideological and political teaching material push method on MOOC online learning platform. Firstly, the learner data is labelled and platform user profiles are constructed by employing the k-means clustering technique. Secondly, by combining significant data block detection methods, significant learning features are extracted. Utilise a polynomial naive Bayes classifier to sort ideological and political teaching materials according to their modality types. Following this, apply collaborative filtering techniques to deliver teaching resources of diverse modalities to learners, aligning with their specific learning preferences. Through experiments, it has been proven that the coverage rate of our method can reach over 90%, with a push delay of only 105 ms and an average absolute error of only 0.13. Inderscience Publishers - linking academia, business and industry through research

Title: Study on multimodal ideological and political teaching material push on MOOC online learning platform

Authors: Yiming Qu; Yanhua Wang

Addresses: The School of Marxism, Henan Polytechnic, Henan, Zhengzhou, 450000, China ' Digital Technology School, Sias University, Zhengzhou, 45000, Henan, China

Abstract: The expected goal is to address the issues of low coverage, high latency, and high average absolute error in traditional push methods for ideological and political teaching resources. This study focuses on the multimodal ideological and political teaching material push method on MOOC online learning platform. Firstly, the learner data is labelled and platform user profiles are constructed by employing the k-means clustering technique. Secondly, by combining significant data block detection methods, significant learning features are extracted. Utilise a polynomial naive Bayes classifier to sort ideological and political teaching materials according to their modality types. Following this, apply collaborative filtering techniques to deliver teaching resources of diverse modalities to learners, aligning with their specific learning preferences. Through experiments, it has been proven that the coverage rate of our method can reach over 90%, with a push delay of only 105 ms and an average absolute error of only 0.13.

Keywords: MOOC online learning platform; multimodal; ideological and political teaching resources; user profiles; significant data block detection.

DOI: 10.1504/IJBIDM.2026.153564

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.8, pp.55 - 74

Received: 03 Jul 2025
Accepted: 31 Oct 2025
Published online: 14 May 2026 *

Title: Study on multimodal ideological and political teaching material push on MOOC online learning platform

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