Title: Method for mining students' online English learning intention based on user portrait and big data

Authors: Yanli Li; Lili Wang; Haitao Gao; Bin Zhang

Addresses: College of Foreign Languages, Tangshan University, Tangshan, 063000, China ' College of Foreign Languages, Tangshan University, Tangshan, 063000, China ' College of Foreign Languages, Tangshan University, Tangshan, 063000, China ' Department of Technology, Intelligent Instrument Factory, North China University of Science and Technology, Tangshan, 063000, China

Abstract: To overcome the problems of low recall, low accuracy, and long time in traditional methods, a new method for mining students' online English learning intention based on user portrait and big data is proposed. With the support of big data technology, the maximum mean difference algorithm is used to determine the distance between student online English learning data sample points, and the K-means algorithm is used to implement student online English learning data collection. The collected data is used to construct user personas, and the attention mechanism is used to extract students' online English learning characteristics. A student's online English learning willingness mining model based on extreme learning machine network is established to obtain relevant mining results. Experimental tests have shown that the recall rate of the proposed method is always above 97.3%, the maximum mining accuracy is 98.1%, and the average mining time is 79.15 ms.

Keywords: user portrait; big data; students; online English; learning intention; maximum mean difference algorithm; attention mechanism; extreme learning machine.

DOI: 10.1504/IJBIDM.2025.145355

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.3/4, pp.413 - 430

Received: 23 Jan 2024
Accepted: 03 Aug 2024
Published online: 31 Mar 2025 *

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