Article: Mining of tourism English learning mode based on temporal clustering and ensemble learning Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.34 pp.45 - 59 Abstract: With the increasing demand for tourism English learning, traditional learning mode analysis methods have limitations in capturing dynamic behaviour and personalised recommendations. This article proposes a tourism English learning mode mining framework that integrates temporal clustering and ensemble learning, aiming to extract multidimensional learning features from time series data and construct a high-precision prediction model. Firstly, the behaviour trajectory of learners is segmented using temporal clustering algorithm to identify their time distribution characteristics and knowledge mastery rhythm at different learning stages. Secondly, an ensemble learning model is used to fuse multi-dimensional features of clustering results, achieving learning effect prediction and pattern classification. In addition, the study revealed the nonlinear correlation between contextualised vocabulary memory and listening and speaking ability development in tourism English learning, providing data-driven decision support for the development of adaptive learning systems. Inderscience Publishers - linking academia, business and industry through research

Title: Mining of tourism English learning mode based on temporal clustering and ensemble learning

Authors: Yan Jing

Addresses: School of Tourism Foreign Languages, Zhengzhou Tourism College, Zhengzhou, 451464, China

Abstract: With the increasing demand for tourism English learning, traditional learning mode analysis methods have limitations in capturing dynamic behaviour and personalised recommendations. This article proposes a tourism English learning mode mining framework that integrates temporal clustering and ensemble learning, aiming to extract multidimensional learning features from time series data and construct a high-precision prediction model. Firstly, the behaviour trajectory of learners is segmented using temporal clustering algorithm to identify their time distribution characteristics and knowledge mastery rhythm at different learning stages. Secondly, an ensemble learning model is used to fuse multi-dimensional features of clustering results, achieving learning effect prediction and pattern classification. In addition, the study revealed the nonlinear correlation between contextualised vocabulary memory and listening and speaking ability development in tourism English learning, providing data-driven decision support for the development of adaptive learning systems.

Keywords: temporal clustering; ensemble learning; attention mechanism; tourism English.

DOI: 10.1504/IJICT.2025.148822

International Journal of Information and Communication Technology, 2025 Vol.26 No.34, pp.45 - 59

Received: 16 May 2025
Accepted: 29 May 2025
Published online: 26 Sep 2025 *

Title: Mining of tourism English learning mode based on temporal clustering and ensemble learning

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