<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Teaching intervention decision is the key to realizing accurate teaching intervention based on big data. This paper portrays students’ English listening learning status and characteristics based on the analysis of students’ behavioral performance data and accordingly provides English listening teaching interventions for different students at the appropriate time. The paper also addresses the problem that the existing big data-based precise teaching interventions are not dynamic enough, introduces reinforcement learning into the teaching intervention decision, and builds a reinforcement learning-based teaching intervention decision model. The mean score of students in the experimental class was 36.31 with a standard deviation of 11.03 on the pre-test and 39.79 with a standard deviation of 6.14 on the post-test, and the mean score of students in the control class was 36.5 with a standard deviation of 10.15 on the pre-test and 36.13 with a standard deviation of 6.14 on the post-test. The difference between the experimental and control classes’ mean scores was only 0.18 on the pre-test and 3.66 on the post-test. Therefore, the precision teaching intervention framework based on big data constructed in this paper has achieved better results in English listening teaching and has strong applicability.</p>
</abstract>

Teaching intervention decision is the key to realizing accurate teaching intervention based on big data. This paper portrays students’ English listening learning status and characteristics based on the analysis of students’ behavioral performance data and accordingly provides English listening teaching interventions for different students at the appropriate time. The paper also addresses the problem that the existing big data-based precise teaching interventions are not dynamic enough, introduces reinforcement learning into the teaching intervention decision, and builds a reinforcement learning-based teaching intervention decision model. The mean score of students in the experimental class was 36.31 with a standard deviation of 11.03 on the pre-test and 39.79 with a standard deviation of 6.14 on the post-test, and the mean score of students in the control class was 36.5 with a standard deviation of 10.15 on the pre-test and 36.13 with a standard deviation of 6.14 on the post-test. The difference between the experimental and control classes’ mean scores was only 0.18 on the pre-test and 3.66 on the post-test. Therefore, the precision teaching intervention framework based on big data constructed in this paper has achieved better results in English listening teaching and has strong applicability.

Research on the mechanism of cultivating the English listening ability of college students based on the background of big data

Applied Mathematics and Nonlinear Sciences

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

Teaching intervention decision is the key to realizing accurate teaching intervention based on big data. This paper portrays students’ English listening...

Research on the mechanism of cultivating the English listening ability of college students based on the background of big data

Pubblicato online: 09 ago 2023

Pagine: -

Ricevuto: 22 set 2022

Accettato: 17 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00154

Parole chiave
Intensive learning, Behavioral performance data, Big data, Instructional intervention decision model

© 2023 Wenqin Liang, published by Sciendo

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

Research on the mechanism of cultivating the English listening ability of college students based on the background of big data

Pubblicato online: 09 ago 2023

Pagine: -

Ricevuto: 22 set 2022

Accettato: 17 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00154

Parole chiaveIntensive learning, Behavioral performance data, Big data, Instructional intervention decision model

© 2023 Wenqin Liang, published by Sciendo

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

Parole chiave
Intensive learning, Behavioral performance data, Big data, Instructional intervention decision model