The Post Effect of Embedded Neural Network Mandarin Proficiency Test on Chinese Learners

Abstract

In the context of rapid technological advancement, embedded neural network technology is gradually penetrating into the field of education, especially in computer-aided language learning (CALL) platforms, bringing unprecedented changes to Mandarin proficiency testing. This study focuses on the application of embedded neural network technology in Mandarin proficiency testing and its post effects on Chinese language learners, aiming to explore in depth how this emerging technology affects learners’ performance, motivation, and long-term learning outcomes. The core of the research lies in comparing the differences in learner performance, motivation, and subsequent learning behavior between computerized testing using embedded neural network technology and traditional paper-based testing. A comparative analysis of two testing modes was conducted by selecting Chinese learners with different language proficiency levels as samples. Preliminary results show that the Mandarin proficiency test based on embedded neural networks not only significantly improves learners’ learning motivation, but also demonstrates more significant results in improving listening and speaking skills due to its personalized teaching resources and intelligent feedback mechanism. This technology simulates real language environments and provides customized learning paths, effectively enhancing learners’ engagement and learning outcomes.