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<p>In this paper, the dependency relationship between different knowledge points is constructed by statistically counting the correct rate of answering knowledge points at adjacent times, finding the prior knowledge of each knowledge point, and the prior of the prior. The dependencies of each knowledge point are stored as key-value pairs and added into the loss function of the model as constraints, thus completing the construction of the deep learning knowledge tracking model. Aiming at the shortcomings of the deep learning knowledge tracking model, we propose to improve the DT-BKT learning knowledge tracking model and use it to design the teaching system of the university English digital course and analyze the teaching system of the university English digital course by using simulation experiments. The results show that the shortest time for acquiring the digital materials of college English is 12.35s, which is 52.87s shorter than that of the conventional English teaching resources information management system, indicating that the system in this paper is more suitable for the development and innovation of college English teaching in colleges and universities. This study can mobilize students’ willingness to participate in English learning in all aspects and has a promoting effect on the development of university English digital course teaching in colleges and universities.</p>
</abstract>

In this paper, the dependency relationship between different knowledge points is constructed by statistically counting the correct rate of answering knowledge points at adjacent times, finding the prior knowledge of each knowledge point, and the prior of the prior. The dependencies of each knowledge point are stored as key-value pairs and added into the loss function of the model as constraints, thus completing the construction of the deep learning knowledge tracking model. Aiming at the shortcomings of the deep learning knowledge tracking model, we propose to improve the DT-BKT learning knowledge tracking model and use it to design the teaching system of the university English digital course and analyze the teaching system of the university English digital course by using simulation experiments. The results show that the shortest time for acquiring the digital materials of college English is 12.35s, which is 52.87s shorter than that of the conventional English teaching resources information management system, indicating that the system in this paper is more suitable for the development and innovation of college English teaching in colleges and universities. This study can mobilize students’ willingness to participate in English learning in all aspects and has a promoting effect on the development of university English digital course teaching in colleges and universities.

The design of teaching system for university English digital course based on deep learning model

Applied Mathematics and Nonlinear Sciences

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

In this paper, the dependency relationship between different knowledge points is constructed by statistically counting the correct rate of answering knowledge...

The design of teaching system for university English digital course based on deep learning model

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 28 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0204

Parole chiave
Deep learning, DT-BKT model, Loss function, Knowledge tracking, University English, Teaching system

© 2024 Li Fu, published by Sciendo

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

The design of teaching system for university English digital course based on deep learning model

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 28 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0204

Parole chiaveDeep learning, DT-BKT model, Loss function, Knowledge tracking, University English, Teaching system

© 2024 Li Fu, published by Sciendo

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

Parole chiave
Deep learning, DT-BKT model, Loss function, Knowledge tracking, University English, Teaching system