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<p>In the teaching platform, providing students with personalized learning solutions by tracking their learning status is the current trend in the development and research of digital intelligence teaching. In this paper, a Bayesian knowledge tracking model based on multiple interactions is used to construct a knowledge point tracking module, and students’ learning behaviors and memory forgetting factors are simultaneously integrated into the IBKT model to construct the PRL-PLP algorithm, and then a Bayesian knowledge tracking model integrating the behaviors and previous factors is obtained, and real-time prediction of students’ mastery is realized using the extended model. Putting the platform into use, it was observed that the total number of student logins to the platform in two days was 680, with an average of 340 logins per day. After using this educational platform for half a month, the students of class A showed an overall improvement in their English test scores compared to their scores before using the platform, with the number of students scoring less than 60 narrowing down from 5 to 0, and an increase of 5 students between the subsections of 91-100, and an increase in the subsections of 81-90, also from 26 to 43 students. By using the IBKT model in the online teaching platform, teachers and students can receive timely feedback on prediction results, teaching efficiency can be improved, and personalized learning guidance can be provided to students.</p>
</abstract>

In the teaching platform, providing students with personalized learning solutions by tracking their learning status is the current trend in the development and research of digital intelligence teaching. In this paper, a Bayesian knowledge tracking model based on multiple interactions is used to construct a knowledge point tracking module, and students’ learning behaviors and memory forgetting factors are simultaneously integrated into the IBKT model to construct the PRL-PLP algorithm, and then a Bayesian knowledge tracking model integrating the behaviors and previous factors is obtained, and real-time prediction of students’ mastery is realized using the extended model. Putting the platform into use, it was observed that the total number of student logins to the platform in two days was 680, with an average of 340 logins per day. After using this educational platform for half a month, the students of class A showed an overall improvement in their English test scores compared to their scores before using the platform, with the number of students scoring less than 60 narrowing down from 5 to 0, and an increase of 5 students between the subsections of 91-100, and an increase in the subsections of 81-90, also from 26 to 43 students. By using the IBKT model in the online teaching platform, teachers and students can receive timely feedback on prediction results, teaching efficiency can be improved, and personalized learning guidance can be provided to students.

The Construction of Interactive Teaching Platform for College English Based on Digital Intelligence Technology

Concord University College, Fujian Normal University

Applied Mathematics and Nonlinear Sciences

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

In the teaching platform, providing students with personalized learning solutions by tracking their learning status is the current trend in the development and...

The Construction of Interactive Teaching Platform for College English Based on Digital Intelligence Technology

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 22. Dez. 2023

Akzeptiert: 31. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0243

Schlüsselwörter
PRL-PLP, Interactive teaching, IBKT model, Knowledge point tracking, Learning behavior prediction

© 2024 Qian Chen, published by Sciendo

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

The Construction of Interactive Teaching Platform for College English Based on Digital Intelligence Technology

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 22. Dez. 2023

Akzeptiert: 31. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0243

SchlüsselwörterPRL-PLP, Interactive teaching, IBKT model, Knowledge point tracking, Learning behavior prediction

© 2024 Qian Chen, published by Sciendo

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

Schlüsselwörter
PRL-PLP, Interactive teaching, IBKT model, Knowledge point tracking, Learning behavior prediction