<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.</p>
</abstract>

Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.

User Behavior Analysis and Optimization of Japanese Language Online Education Platforms

Media College, ShangHai Donghai Vocational&Technical College

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped...

User Behavior Analysis and Optimization of Japanese Language Online Education Platforms

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 15. Apr. 2024

Akzeptiert: 29. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2042

Schlüsselwörter
RFG-SVM model, FCM algorithm, Association rule algorithm, fsQCA method, Japanese language online education platform

© 2024 Ran Chu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

User Behavior Analysis and Optimization of Japanese Language Online Education Platforms

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 15. Apr. 2024

Akzeptiert: 29. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2042

SchlüsselwörterRFG-SVM model, FCM algorithm, Association rule algorithm, fsQCA method, Japanese language online education platform

© 2024 Ran Chu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
RFG-SVM model, FCM algorithm, Association rule algorithm, fsQCA method, Japanese language online education platform