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Approaches to the Development of Language Intelligence in Multimedia English Teaching from the Perspective of Game Theory

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  • Nan Wei
  • Leipo Liu

Abstract

At present, the majority of English language learners use computers as an aid to support their learning. However, the existing software is relatively homogeneous, which can only correct the pronunciation errors of the English language. Due to the massive number of problems in the software itself, it is difficult for English language learners to correct the errors that occur in their pronunciation properly. In this study, a scoring mechanism is established from the game theory perspective of language intelligent development in combination with multimedia-assisted teaching from three perspectives, that is, acoustics, rhythm, and sense of speech. The output of deep learning is simulated by using network parameters based on language intelligent development to assess the language. Meanwhile, the teaching data and materials for the English language are uploaded and answered online in real time. In this way, students can have access to the course content shared by the teacher, which has a certain auxiliary effect on the English language learning of the students. With the aid of multimedia technology, an excellent English teaching model can be used to enhance the English language learning ability of students effectively and improve their interest and initiative in English learning with the English learning of students as the main body. It can be seen from the results of the simulation that students’ learning efficiency in exploring English language independent learning can be improved effectively mainly by making use of the reference database and aligning it with expert knowledge for error identification.

Suggested Citation

  • Nan Wei & Leipo Liu, 2022. "Approaches to the Development of Language Intelligence in Multimedia English Teaching from the Perspective of Game Theory," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, September.
  • Handle: RePEc:hin:jnlmpe:7762399
    DOI: 10.1155/2022/7762399
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