Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/6748
Title: | A virtual reality-based spoken English learning platform |
Authors: | Wang, Chengyao Lian, Xin Zhuang, Canbiao Dr. KWOK Pak Ki, Alex Yan, Mian |
Issue Date: | 2021 |
Conference: | 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design |
Abstract: | Under economic globalization, the exchange between different countries is becoming more frequent. Increasingly more people worldwide start or prepare to learn second (or foreign) languages. In particular, the English language plays a significant role in globalization progress, given that it has been used as a means for international commerce and communication. However, traditional methods for acquiring a second language (e.g., attending tutorial classes) are costly in terms of time, money, and effort to many people. Given the increasing demand for English language learning and traditional learning methods' pitfalls, this paper introduces a virtual-reality-based platform for learning English. The platform provides virtual scenes of daily life, such as hospitals, supermarkets, and schools. Users can instantly communicate with other online users in the virtual world. Besides, aiming to improve users' learning efficiency, every word uttered is translated into English and users' mother language in real-time. This proposed system has five advantages: 1) It is efficient because users can use it to learn a language at any time and space. They can chat with foreigners online whenever they want and wherever they are. 2) Communicating with online users helps practice one's oral English without the need to find a partner in reality. 3) Instant translation facilitates instant learning and acquisition of the phases and vocabularies. 4) The virtual 3D environment simulating real-life scenes creates an enjoyable atmosphere for users to practice their oral English. 5) The system is universal to learners of different languages since it supports English and many other languages. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/6748 |
DOI: | 10.1109/CSCWD49262.2021.9437696 |
Appears in Collections: | Applied Data Science - Publication |
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