Design and Implementation of WeChat Mini Program for English Vocabulary Learning

In order to solve the problems of fragmented learning content and poor interactivity in traditional English vocabulary learning applications, this paper designs and implements an English vocabulary learning system based on WeChat mini program. The system adopts a front-end and back-end separation architecture, with the front-end implementing user interaction and data binding through WXML, WXSS, and JavaScript, and the back-end exchanging data through API interfaces to ensure efficient data transmission and stable performance. This article analyzes the shortcomings of existing English learning applications and proposes targeted optimization solutions, especially improvements in data loading and request processing. The test results show that the system can still maintain stable response and data consistency of up to 98% under high concurrency conditions. The application of this system in English vocabulary learning has good practical value, which can effectively improve learning efficiency and user experience.

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Publication Details

Published
2025-04-11
DOI
https://doi.org/10.1109/ainit65432.2025.11035898
Primary Topic
Educational Technology and Assessment
Type
article
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article

Design and Implementation of WeChat Mini Program for English Vocabulary Learning

Gang Hu, Rongrong Guo
Educational Technology and Assessment
article

Design and Implementation of WeChat Mini Program for English Vocabulary Learning

Gang Hu, Rongrong Guo
article en

Abstract

In order to solve the problems of fragmented learning content and poor interactivity in traditional English vocabulary learning applications, this paper designs and implements an English vocabulary learning system based on WeChat mini program. The system adopts a front-end and back-end separation architecture, with the front-end implementing user interaction and data binding through WXML, WXSS, and JavaScript, and the back-end exchanging data through API interfaces to ensure efficient data transmission and stable performance. This article analyzes the shortcomings of existing English learning applications and proposes targeted optimization solutions, especially improvements in data loading and request processing. The test results show that the system can still maintain stable response and data consistency of up to 98% under high concurrency conditions. The application of this system in English vocabulary learning has good practical value, which can effectively improve learning efficiency and user experience.

Kingston Technology (United States) (US)
Quality Education
Openalex Percentile: Top 79%
Educational Technology and Assessment
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