Analysis of an Online English Teaching Model Application Based on Improved Multiorganizational Particle Population...

Area of Science:

• Educational Technology
• Computational Intelligence
• Applied Linguistics

Background:

• Traditional English speaking instruction faces challenges in engagement and skill development.
• Online and blended learning models offer potential solutions for improving language acquisition.
• Optimization algorithms can enhance the design and effectiveness of educational models.

Purpose of the Study:

• To develop and refine a blended learning model for junior high English speaking instruction.
• To apply an improved multiorganizational particle population optimization algorithm in model development.
• To evaluate the effectiveness of the proposed blended learning model on students' speaking skills and learning attitudes.

Main Methods:

• Utilized an improved multiorganizational particle population optimization algorithm.
• Employed a three-round action research methodology for model development and refinement.
• Collected data through pre- and post-tests on English speaking skills and relevant questionnaires.

Main Results:

• The developed blended learning model significantly improved students' English speaking skills (pronunciation, intonation, communication, expression).
• Students showed increased interest in learning English and more positive attitudes towards the subject.
• Enhancements were observed in students' group cooperation, communication, independent learning, and evaluation abilities.

Conclusions:

• The blended learning-based speaking teaching model is effective for junior high English as a foreign language education.
• The improved optimization algorithm and dimensional learning strategy contribute to a more robust and effective learning model.
• The model successfully addresses limitations of traditional methods and enhances multiple facets of student learning.