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

<p>This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs the evaluation index system of BOPPPS blended English teaching based on the teaching data. Then, it constructs the teaching effect evaluation model based on an improved BP neural network, completes the evaluation of the BOPPPS blended English teaching effect, optimizes the design of university English blended teaching based on the BOPPPS model and evaluates the learning effectiveness based on teaching experiments. The students in the experimental class were clustered into three categories before the experiment: “excellent students” (17.77%), “average students” (55.56%), and “failing students” (26.67%). 26.67%). Students in classes A and B increased by 1.22 and 0.16 points, respectively. In comparison, the progress of students in class A is more obvious than that of students in class B (P=0.026&lt;0.05), indicating that there is a significant difference between the performance of class A and class B. The evidence suggests that BOPPPS blended English teaching can significantly enhance students’ performance, and the quality of instruction is superior.</p>
</abstract>

This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs the evaluation index system of BOPPPS blended English teaching based on the teaching data. Then, it constructs the teaching effect evaluation model based on an improved BP neural network, completes the evaluation of the BOPPPS blended English teaching effect, optimizes the design of university English blended teaching based on the BOPPPS model and evaluates the learning effectiveness based on teaching experiments. The students in the experimental class were clustered into three categories before the experiment: “excellent students” (17.77%), “average students” (55.56%), and “failing students” (26.67%). 26.67%). Students in classes A and B increased by 1.22 and 0.16 points, respectively. In comparison, the progress of students in class A is more obvious than that of students in class B (P=0.026<0.05), indicating that there is a significant difference between the performance of class A and class B. The evidence suggests that BOPPPS blended English teaching can significantly enhance students’ performance, and the quality of instruction is superior.

Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling

Applied Mathematics and Nonlinear Sciences

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

This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs...

Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling

Published Online: Jul 05, 2024

Page range: -

Received: Mar 18, 2024

Accepted: Jun 05, 2024

DOI: https://doi.org/10.2478/amns-2024-1624

Keywords
K-means clustering algorithm, BOPPPS blended teaching, BP neural network, Teaching effect evaluation

© 2024 Yan Liu, published by Sciendo

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

Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling

Published Online: Jul 05, 2024

Page range: -

Received: Mar 18, 2024

Accepted: Jun 05, 2024

DOI: https://doi.org/10.2478/amns-2024-1624

KeywordsK-means clustering algorithm, BOPPPS blended teaching, BP neural network, Teaching effect evaluation

© 2024 Yan Liu, published by Sciendo

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

Keywords
K-means clustering algorithm, BOPPPS blended teaching, BP neural network, Teaching effect evaluation