Design and Research of Computer-Aided Translation Teaching Course under the Background of Embedded Microprocessor Wireless Communication

Yuan, Xiujuan; Zhu, Ruike

doi:10.1155/2022/8594212

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…e experimental results show that the student concentration on computer-assisted teaching is 20% higher than that of traditional teaching, and other relevant data is more than 8% higher. Students bene t from the teaching of computer-assisted translation courses [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Oral English Auxiliary Teaching System Based on Deep Learning

Qu¹,

Li²

2022

Advances in Multimedia

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In order to solve the problem of the oral English auxiliary teaching system, a research based on Deep Learning was proposed. Based on the theory of Deep Learning, the teaching mode of Deep Learning for college students built on information technology was investigated in the research. As for Shallow Strategy, the item “I think that the best way to pass the English examination is to keep high frequency words in mind” changed most significantly from 3.3 to 2.68. As for Deep Strategy, the most significant change was the item “It can be very interesting for almost all the oral English topics as long as you engage in it actively.” The value increased from 3.31 to 3.97, which was almost close to 4. From the comparison, it could be found that the students changed from memorizing English words mechanically and passively in order to pass the exam to engaging in the oral English situation actively to solve problems so as to obtain self-satisfaction. As a language, English can reflect a unique cultural heritage. It is different from Chinese culture, which can improve learners’ spiritual and cultural accomplishments subtly. In addition, we should try to solve problems with English logical thinking, which can train our critical thinking and oral expression ability, enhance our self-confidence, and improve our sense of self-efficacy. A design-based research paradigm was used in the research. Through the integrated use of information technology, it aimed at building out a deeply mixed teaching mode based on “Cloud Class and Offline Class.” A combination of quantitative and qualitative data collection methods was adopted to evaluate the practical effect. According to the experimental results, two rounds of mixed-mode iterative loop design were performed for the mixed teaching mode. In order to make it more operable, it was improved and perfected continuously.

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Section: Literature Reviewmentioning

confidence: 99%

Oral English Auxiliary Teaching System Based on Deep Learning

Qu¹,

Li²

2022

Advances in Multimedia

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“…By leveraging this data, these systems can provide tailored feedback and guidance to English learners, helping them improve their spoken language skills with precision and efficiency. One of the key applications of machine learning in computer-aided English spoken teaching is in speech recognition and analysis [17]. Advanced speech recognition algorithms can accurately transcribe spoken English, detect pronunciation errors, and identify areas for improvement.…”

Section: Introductionmentioning

confidence: 99%

Application of Computer-Aided Technology in Teaching Spoken English

Liu

2024

jes

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Computer-aided technology has revolutionized the teaching of spoken English by offering innovative tools and resources that enhance the learning experience. Through interactive software and multimedia platforms, learners can engage in immersive language exercises, receive real-time feedback, and access personalized instruction tailored to their proficiency levels. This paper introduces a groundbreaking approach for integrating computer-aided technology into the teaching of spoken English, utilizing Automated Probabilistic Markov Chain Machine Learning (APMC-ML). Recognizing the significance of effective communication skills in the modern world, particularly in the realm of English language proficiency, this research endeavors to enhance the pedagogical process through innovative technological means. The methodology involves the development and implementation of an interactive computer-aided system that leverages APMC-ML algorithms to facilitate language learning. This system encompasses various modules designed to cater to different aspects of spoken English acquisition, including pronunciation, vocabulary, fluency, and conversational skills. Through a user-friendly interface, learners are provided with personalized learning experiences tailored to their individual proficiency levels and learning preferences. The application of APMC-ML in teaching spoken English represents a paradigm shift in language education, offering a data-driven approach that complements traditional teaching methods. By harnessing the power of machine learning and probabilistic modeling, educators can optimize the learning experience, expedite skill acquisition, and promote greater language proficiency among learners.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

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confidence: 99%

Retracted: Design and Research of Computer-Aided Translation Teaching Course under the Background of Embedded Microprocessor Wireless Communication

2023

Scientific Programming

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Design and Research of Computer-Aided Translation Teaching Course under the Background of Embedded Microprocessor Wireless Communication

Cited by 4 publications

References 19 publications

Oral English Auxiliary Teaching System Based on Deep Learning

Oral English Auxiliary Teaching System Based on Deep Learning

Application of Computer-Aided Technology in Teaching Spoken English

Retracted: Design and Research of Computer-Aided Translation Teaching Course under the Background of Embedded Microprocessor Wireless Communication

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