Analysis of English Cultural Teaching Model Based on Machine Learning

Area of Science:

• Information Technology
• Data Science
• Artificial Intelligence

Background:

• Global mobile phone usage is widespread, with nearly five billion users. Monthly data consumption per user averages 45 exabytes, highlighting the scale of digital information generation.
• The increasing reliance on data analytics is critical for e-commerce, enabling prediction of user behavior and future activities.
• The shift towards online education, accelerated by the pandemic, necessitates robust data handling and analysis capabilities.

Purpose of the Study:

• To explore the challenges and implications of managing and analyzing massive datasets generated by global mobile phone usage.
• To investigate the application of machine learning and artificial intelligence in understanding and predicting user activity based on collected data.
• To analyze the effectiveness of online teaching models and the role of big data in educational advancements.

Main Methods:

• Data collection and analysis of mobile phone usage patterns, estimating monthly data consumption per user.
• Application of machine learning algorithms, specifically Support Vector Machine (SVM), for data analysis.
• Comparison of the proposed SVM model with traditional fuzzy logic for evaluating English culture teaching.

Main Results:

• The study highlights the immense scale of data generated by mobile phone users, posing challenges for traditional computing systems.
• A Support Vector Machine (SVM) model demonstrated high accuracy (98%) in analyzing English culture teaching data.
• The proposed SVM model outperformed traditional fuzzy logic by 5% in accuracy.

Conclusions:

• The exponential growth in mobile data necessitates advanced analytical approaches beyond traditional systems.
• Machine learning models like SVM show significant promise for accurate data analysis in various fields, including education.
• The findings support the integration of data-driven strategies in both e-commerce and the evolving landscape of online education.