Treffer: Optimization of Computer-Assisted English Teaching System Under VC Visualization Software.

At present, building an information-based teaching platform for auxiliary teaching in a network environment has become a mainstream teaching method for English teaching in various schools. How to combine various multimedia teaching instruments with English classroom teaching, excite students' enthusiasm for English, and change the traditional classroom teaching model has turn into the main goal of the current English teaching reform. Visual C++ (VC) visualization is an object-oriented visualization programming language, which is widely used in graphics and images, networks and communications, and control. It has the strong point of stability and running speed rapidly, and can be used from the bottom layer to the rapid development of user-oriented software. The effective integration of VC visualization technology into English teaching can enhance the learning capability of students in the field of independent and collaborative. Therefore, based on the current status of English teaching reform, this research analyzed the needs of the current computer-assisted English teaching system design, and designed the B/S (client/server) model with the support of VC visualization software to optimize the computer-assisted English teaching system. After the feasibility analysis and overall architecture design of the system, the realization of several main functional modules, core codes and interface diagrams were studied, and the system was tested according to the software testing theory test cases. The application of this system could enhance the actual teaching efficiency, bring into play the value of computer-assisted English teaching simultaneously, increase the level of English teaching, exert the benefits of active application. [ABSTRACT FROM AUTHOR]

Copyright of Computer-Aided Design & Applications is the property of Computer-Aided Design & Applications 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.)