Article: Research on multi-modal teaching resource association resource mining under MOOC ideological and political learning Journal: International Journal of Computer Applications in Technology (IJCAT) 2026 Vol.78 No.7 pp.19 - 26 Abstract: To overcome the limitations of current mining algorithms and improve the effectiveness of resource mining, this paper proposes a multimodal teaching resource association resource mining algorithm for MOOC ideological and political learning. Firstly, the features of text, image and audio modalities are extracted using the bag of words model, VGG16 network and Mel frequency cepstral coefficient method. Secondly, the feature vectors of each modality are concatenated and fused. Owing to the high dimensionality after fusion, principal component analysis is used for dimensionality reduction. Finally, feature fusion, dimensionality reduction and association rule mining are used to optimise the association of multimodal teaching resources, and dynamic association rules are introduced to adapt to the dynamic needs of students' learning process, thereby improving the effectiveness of MOOC ideological and political learning. The experimental results show that the mining results of the proposed algorithm have diversity and strong correlation with the target topic. Inderscience Publishers - linking academia, business and industry through research

Title: Research on multi-modal teaching resource association resource mining under MOOC ideological and political learning

Authors: Hui Wang

Addresses: College of Marxism, Jilin Institute of Physical Education, Changchun, Jilin, China

Abstract: To overcome the limitations of current mining algorithms and improve the effectiveness of resource mining, this paper proposes a multimodal teaching resource association resource mining algorithm for MOOC ideological and political learning. Firstly, the features of text, image and audio modalities are extracted using the bag of words model, VGG16 network and Mel frequency cepstral coefficient method. Secondly, the feature vectors of each modality are concatenated and fused. Owing to the high dimensionality after fusion, principal component analysis is used for dimensionality reduction. Finally, feature fusion, dimensionality reduction and association rule mining are used to optimise the association of multimodal teaching resources, and dynamic association rules are introduced to adapt to the dynamic needs of students' learning process, thereby improving the effectiveness of MOOC ideological and political learning. The experimental results show that the mining results of the proposed algorithm have diversity and strong correlation with the target topic.

Keywords: MOOC; ideological and political education; multimodal; teaching resources; resource mining; principal component analysis; association rules.

DOI: 10.1504/IJCAT.2026.153764

International Journal of Computer Applications in Technology, 2026 Vol.78 No.7, pp.19 - 26

Received: 11 Jun 2025
Accepted: 22 Sep 2025
Published online: 26 May 2026 *

Title: Research on multi-modal teaching resource association resource mining under MOOC ideological and political learning

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