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

<p>Artificial intelligence technology is increasingly being employed within educational contexts, markedly enhancing the dynamics of instruction through the integration of intelligent algorithms. This study endeavors to revitalize the BOPPPS model in blended college English teaching by amalgamating it with the Bayesian knowledge tracking model and a reinforcement learning algorithm. The objective is to establish a refined, blended teaching framework based on the BOPPPS model and to investigate its efficacy along with the variables that influence its outcomes. The findings affirm a positive correlation between the effectiveness of blended English teaching at the university level and the student's cognitive abilities, skillsets, and affective attitudes, with all p-values demonstrating significance at the 0.001 level. Comparative analysis of pre-and post-test data from the blended teaching experiment revealed no initial significant differences between the experimental and control groups across six dimensions of English proficiency and five dimensions of interest in learning English. However, post-experiment results indicated substantial enhancements in the experimental group's overall English scores, listening, reading comprehension, writing, translation, and speaking abilities relative to the control group, with p-values falling below 0.05. Additionally, the p-values for the paired sample tests concerning the dimensions of interest in learning English in both groups were also below 0.05. These results suggest that blended English teaching at the university level not only significantly boosts students’ performance in English but also augments their interest in the language. This study underscores the potential of integrated teaching models that incorporate AI-driven algorithms to significantly enhance educational outcomes.</p>
</abstract>

Artificial intelligence technology is increasingly being employed within educational contexts, markedly enhancing the dynamics of instruction through the integration of intelligent algorithms. This study endeavors to revitalize the BOPPPS model in blended college English teaching by amalgamating it with the Bayesian knowledge tracking model and a reinforcement learning algorithm. The objective is to establish a refined, blended teaching framework based on the BOPPPS model and to investigate its efficacy along with the variables that influence its outcomes. The findings affirm a positive correlation between the effectiveness of blended English teaching at the university level and the student's cognitive abilities, skillsets, and affective attitudes, with all p-values demonstrating significance at the 0.001 level. Comparative analysis of pre-and post-test data from the blended teaching experiment revealed no initial significant differences between the experimental and control groups across six dimensions of English proficiency and five dimensions of interest in learning English. However, post-experiment results indicated substantial enhancements in the experimental group's overall English scores, listening, reading comprehension, writing, translation, and speaking abilities relative to the control group, with p-values falling below 0.05. Additionally, the p-values for the paired sample tests concerning the dimensions of interest in learning English in both groups were also below 0.05. These results suggest that blended English teaching at the university level not only significantly boosts students’ performance in English but also augments their interest in the language. This study underscores the potential of integrated teaching models that incorporate AI-driven algorithms to significantly enhance educational outcomes.

Strategies for Applying BOPPPS Model Supported by Intelligent Algorithms in Blended Teaching of College English

School of Foreign Languages, Chengdu Technological University

Applied Mathematics and Nonlinear Sciences

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

Artificial intelligence technology is increasingly being employed within educational contexts, markedly enhancing the dynamics of instruction through the...

Strategies for Applying BOPPPS Model Supported by Intelligent Algorithms in Blended Teaching of College English

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 19 feb 2024

Accettato: 16 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1719

Parole chiave
BOPPPS model, Bayesian model, Reinforcement learning, English teaching

© 2024 Yi Ou., published by Sciendo

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

Strategies for Applying BOPPPS Model Supported by Intelligent Algorithms in Blended Teaching of College English

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 19 feb 2024

Accettato: 16 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1719

Parole chiaveBOPPPS model, Bayesian model, Reinforcement learning, English teaching

© 2024 Yi Ou., published by Sciendo

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

Parole chiave
BOPPPS model, Bayesian model, Reinforcement learning, English teaching