<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper firstly constructs a foreign language subject system according to the foreign language teaching objectives and students’ learning situation in colleges and universities, puts forward a policy of informatization of foreign language teaching, and summarizes the ways in which college students’ foreign language learning behavior in the era of big data. Secondly, on the basis of corpus technology, the word vectorization representation of foreign language utterances is carried out, followed by similarity calculation of the word vectorization representation, judging the type of foreign language learning according to the results of foreign language semantic similarity calculation, and calculating the maximum weight path of the word vector sequence by using dynamic planning algorithm. Then, according to the demand analysis of the foreign language teaching corpus, the foreign language teaching corpus is constructed, and the application analysis of the corpus of foreign language teaching is carried out. The results indicate that the students in both classes have a similar understanding of the meaning and lexical properties of vocabulary. However, there is a certain gap in the collocation and utilization of vocabulary, and the corpus-based vocabulary teaching method is more conducive to students’ mastery of the target vocabulary than the traditional vocabulary teaching method, and the level of vocabulary learning is comparatively higher and more effective. The quality of foreign language teaching in colleges and universities can be improved by reference to this study.</p>
</abstract>

This paper firstly constructs a foreign language subject system according to the foreign language teaching objectives and students’ learning situation in colleges and universities, puts forward a policy of informatization of foreign language teaching, and summarizes the ways in which college students’ foreign language learning behavior in the era of big data. Secondly, on the basis of corpus technology, the word vectorization representation of foreign language utterances is carried out, followed by similarity calculation of the word vectorization representation, judging the type of foreign language learning according to the results of foreign language semantic similarity calculation, and calculating the maximum weight path of the word vector sequence by using dynamic planning algorithm. Then, according to the demand analysis of the foreign language teaching corpus, the foreign language teaching corpus is constructed, and the application analysis of the corpus of foreign language teaching is carried out. The results indicate that the students in both classes have a similar understanding of the meaning and lexical properties of vocabulary. However, there is a certain gap in the collocation and utilization of vocabulary, and the corpus-based vocabulary teaching method is more conducive to students’ mastery of the target vocabulary than the traditional vocabulary teaching method, and the level of vocabulary learning is comparatively higher and more effective. The quality of foreign language teaching in colleges and universities can be improved by reference to this study.

Foreign Language Teaching and Learning Behaviour with a Big Data Corpus

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Foreign Language Teaching and Learning Behaviour with a Big Data Corpus"}

This paper firstly constructs a foreign language subject system according to the foreign language teaching objectives and students’ learning situation in...

Foreign Language Teaching and Learning Behaviour with a Big Data Corpus

Pubblicato online: 08 nov 2023

Pagine: -

Ricevuto: 21 dic 2022

Accettato: 19 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.01061

Parole chiave
Dynamic programming algorithm, Word vector sequences, Semantic similarity, Corpus, Foreign language teaching

© 2023 Yan Li et al., published by Sciendo

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

Foreign Language Teaching and Learning Behaviour with a Big Data Corpus

Pubblicato online: 08 nov 2023

Pagine: -

Ricevuto: 21 dic 2022

Accettato: 19 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.01061

Parole chiaveDynamic programming algorithm, Word vector sequences, Semantic similarity, Corpus, Foreign language teaching

© 2023 Yan Li et al., published by Sciendo

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

Parole chiave
Dynamic programming algorithm, Word vector sequences, Semantic similarity, Corpus, Foreign language teaching