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Design of College English Online Learning Platform Based on MVC Framework.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chi, Meiying 3 of 3

Abstract

The rapid development of online learning platforms has transformed traditional English education by enabling flexibility, affordability, and accessibility. This study introduces an innovative English online learning platform, designed using the model–view–controller (MVC) framework — a software architectural pattern that separates the application into three interconnected components: Model (data management), view (user interface), and controller (business logic) — and a three-tier architecture. The platform integrates five core functions: user service, educational administration, online learning, platform operation management, and course recommendation. A novel English online test system was developed within this framework, featuring an intelligent recommendation engine that combines association rule-based and user-based collaborative filtering (CF) algorithms to enhance personalized learning. Comprehensive system analysis, including requirements specification, modular construction, database design, and user interface development, was conducted. Experimental evaluations demonstrate improved recommendation accuracy and system performance, offering valuable insights for optimizing English learning platforms. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/12, Vol. 34, Issue 4, p1
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156425402980
  • Accession Number:186254815
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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