Mining the English application learning patterns of college students based on time series clustering

Article type: Research Article

Authors: Niu, Lili^{; *}

Affiliations: College of Foreign Language, Xinxiang Institute of Engineering, Xinxiang, China

Correspondence: [*] Corresponding author. Lili Niu. E-mail: [email protected].

Abstract: As a convenient learning tool in the We Media era, mobile apps have been paid more and more attention by college students because of their accompanying timeliness and practicality. With the increasing number of English learning apps, many such apps provide college students with new ways to obtain learning resources and diversified learning modes. The related research in the field of mobile-assisted language learning at home and abroad has developed over nearly 20 years, basically following the route from theory to application in practice, but there have been few process studies on learners’ individual language skill learning behaviors based on mobile platform data. In this study, the time series clustering method was adopted, and the learning behavior of college students in an English vocabulary learning app in China was selected for data mining. Firstly, taking the “single-day memorization amount” as the measurement index, the memorization records of college students in the whole use cycle were extracted and processed into trajectory data, and the KmL algorithm was used to cluster the trajectory of the memorization amount in the time series. According to the intra-class average trajectory, the characteristics of learning behavior changes among the different college students are summarized, and two learning modes are depicted. Secondly, through the experimental analysis, it was found that adopting the English learning model three weeks before an exam can effectively stimulate college students and improve their willingness to learn and continue using the app.

Keywords: Time series clustering, English app, data mining, learning mode

DOI: 10.3233/JIFS-231476

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7691-7700, 2023