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<p>The rapid development of information technology has brought new opportunities to the field of language learning and formed new learning methods. In this paper, a content-based quantum recommendation algorithm is first proposed to calculate the preference attributes of English learners using the Hamming distance and then combined with the constructed dynamic interest model of learners to complete the personalized recommendation of English language learning resources. The algorithm is employed to build an English-language customized learning system that incorporates learning, resource management, and proficiency assessment functions, and performance testing is carried out simultaneously. Furthermore, the study utilizes the system to conduct application experiments to evaluate its improvement in English language learning efficiency. The results showed that the mean value of perceived satisfaction with the improvement of English language efficiency in classes using the system was 7.48, and the ANOVA results (F=3.88, p&lt;0.5) indicated a significant difference from the traditional method. Among them, the performance enhancement rates of superior and intermediate students were more than 45% and 62%, respectively, which effectively enhanced the efficiency of English language learning. After this study, it can provide a high reference value for the application of quantum machine algorithms in the field of education.</p>
</abstract>

The rapid development of information technology has brought new opportunities to the field of language learning and formed new learning methods. In this paper, a content-based quantum recommendation algorithm is first proposed to calculate the preference attributes of English learners using the Hamming distance and then combined with the constructed dynamic interest model of learners to complete the personalized recommendation of English language learning resources. The algorithm is employed to build an English-language customized learning system that incorporates learning, resource management, and proficiency assessment functions, and performance testing is carried out simultaneously. Furthermore, the study utilizes the system to conduct application experiments to evaluate its improvement in English language learning efficiency. The results showed that the mean value of perceived satisfaction with the improvement of English language efficiency in classes using the system was 7.48, and the ANOVA results (F=3.88, p<0.5) indicated a significant difference from the traditional method. Among them, the performance enhancement rates of superior and intermediate students were more than 45% and 62%, respectively, which effectively enhanced the efficiency of English language learning. After this study, it can provide a high reference value for the application of quantum machine algorithms in the field of education.

A study on the application of quantum computing in improving the efficiency of English language learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The rapid development of information technology has brought new opportunities to the field of language learning and formed new learning methods. In this paper,...

A study on the application of quantum computing in improving the efficiency of English language learning

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 22. Feb. 2024

Akzeptiert: 05. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1358

Schlüsselwörter
Quantum computing, Recommendation algorithm, Hamming distance, Dynamic interest model, Personalized learning system

© 2024 Fei Lei, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A study on the application of quantum computing in improving the efficiency of English language learning

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 22. Feb. 2024

Akzeptiert: 05. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1358

SchlüsselwörterQuantum computing, Recommendation algorithm, Hamming distance, Dynamic interest model, Personalized learning system

© 2024 Fei Lei, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Quantum computing, Recommendation algorithm, Hamming distance, Dynamic interest model, Personalized learning system