Personalized College English Learning Based on Deep Learning under the Background of Big Data

Area of Science:

• Educational Technology
• Artificial Intelligence
• Applied Linguistics

Background:

• The increasing demand for English proficiency in globalized industries necessitates improved college English education.
• Traditional English courses often lack personalization, failing to meet diverse student needs.
• Deep learning offers a potential solution for tailoring educational experiences.

Purpose of the Study:

• To investigate the application of deep learning methods for personalized learning in college English courses.
• To analyze the effectiveness of deep learning in improving students' English listening and reading skills.
• To reduce teacher workload and enhance student-centered learning.

Main Methods:

• Utilizing deep learning algorithms to develop personalized English learning plans.
• Analyzing deep learning neural network models for English speech recognition.
• Extracting characteristic parameters of English speech.
• Comparing deep learning with shallow learning methodologies.
• Conducting random sampling for effect analysis.

Main Results:

• Deep learning effectively supports personalized learning in college English.
• The study demonstrates improvements in both listening comprehension and reading practice outcomes.
• The application of deep learning reduces teacher workload, allowing for greater focus on individual student requirements.

Conclusions:

• Deep learning is a viable and effective approach for personalized college English learning.
• This methodology enhances learning efficiency and student outcomes in English language acquisition.
• Personalized learning plans based on deep learning benefit both students and educators.