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<p>The development of informatization in education has provided the possibility of using resource libraries to enhance English translation courses. Facing this new demand, this paper adopts an entity-based approach to connect multimodal entities with other structured entities, to construct a multimodal knowledge graph for English translation courses in colleges and universities. On this basis, the recommendation model of collaborative filtering is introduced to provide students with personalized English translation exercise recommendation services according to the mastery of knowledge points processed by the knowledge graph. After the model is designed, the knowledge points of the English translation course in a university are firstly extracted physically, and then two classes are selected in the university for controlled experiments. The number of successes in terms of vertical depth strategy is 264, the success rate is 88%, and the success rate of recommendation based on the centrality of knowledge points and contribution value is 94.66%. The mean score of the experimental class was 72.12 and the control class was 67.48. The significant difference in the scores can be confirmed by the fact that the p-value of the independent square root test for the two classes was less than 0.5. English teachers can benefit from this model’s assistance in evaluating English translation resources and providing a variety of teaching methods.</p>
</abstract>

The development of informatization in education has provided the possibility of using resource libraries to enhance English translation courses. Facing this new demand, this paper adopts an entity-based approach to connect multimodal entities with other structured entities, to construct a multimodal knowledge graph for English translation courses in colleges and universities. On this basis, the recommendation model of collaborative filtering is introduced to provide students with personalized English translation exercise recommendation services according to the mastery of knowledge points processed by the knowledge graph. After the model is designed, the knowledge points of the English translation course in a university are firstly extracted physically, and then two classes are selected in the university for controlled experiments. The number of successes in terms of vertical depth strategy is 264, the success rate is 88%, and the success rate of recommendation based on the centrality of knowledge points and contribution value is 94.66%. The mean score of the experimental class was 72.12 and the control class was 67.48. The significant difference in the scores can be confirmed by the fact that the p-value of the independent square root test for the two classes was less than 0.5. English teachers can benefit from this model’s assistance in evaluating English translation resources and providing a variety of teaching methods.

Diversified Teaching of English Translation Courses in Colleges and Universities Based on the Integration of Multiple Features

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The development of informatization in education has provided the possibility of using resource libraries to enhance English translation courses. Facing this...

Diversified Teaching of English Translation Courses in Colleges and Universities Based on the Integration of Multiple Features

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 22. Dez. 2023

Akzeptiert: 28. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0275

Schlüsselwörter
Multi-feature fusion, Knowledge graph, Recommendation model, Collaborative filtering, English translation

© 2024 Yang Yang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Diversified Teaching of English Translation Courses in Colleges and Universities Based on the Integration of Multiple Features

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 22. Dez. 2023

Akzeptiert: 28. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0275

SchlüsselwörterMulti-feature fusion, Knowledge graph, Recommendation model, Collaborative filtering, English translation

© 2024 Yang Yang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Multi-feature fusion, Knowledge graph, Recommendation model, Collaborative filtering, English translation