Mandarin learning app for english-speaking children

Chang, Joo Yee (2025) Mandarin learning app for english-speaking children. Final Year Project, UTAR.

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Abstract

Mandarin has become one of the most important languages in the world due to China's significant influence on economics, politics, and culture. Although its economic and political rise has increased its importance, learning Mandarin as a second or foreign language remains a considerable challenge, particularly for native English-speaking children due to tonal variations, complex grammar, and limited access to engaging and age-appropriate resources. However, most existing learning applications, such as Duolingo, HelloChinese, Studycat, and ChineseSkill, provide learning materials that teach Mandarin via English but lack essential features, including real-time translation, text or image summarization, and personalized learning paths. These applications often prioritize vocabulary over critical language skills, such as pronunciation and grammar, while adopting a one-size-fits-all approach that does not adapt to individual proficiency levels. This project aims to deliver a user-friendly mobile application that helps English-speaking children aged 10 to 12 effectively learn Mandarin through English as an assisting language, addressing limitations in existing applications. The proposed application provides interactive lessons and exercises that cover Pinyin, Chinese characters, grammar, and speaking skills. Advanced features such as real-time translation and a summarization tool are integrated into the proposed application for self-directed learning. It also includes interactive elements, such as real-time feedback and a reward system, to motivate the children throughout the learning process. To ensure personalization, the system includes an entry-level test and leverages a Support Vector Machine model to predict learner performance. This model achieved 97% accuracy and 95.65% F1-score, allowing the application to recommend target review sessions for learners predicted to perform poorly. The proposed application is developed using agile methodology and technologies such as Android Studio, Amazon Web Services, Firebase, Jupiter Notebook, the Gemini API, FastAPI and Google Translation packages.

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