Design and Development of Smart Learning System for College English based on Intelligent Cloud Platform

A smart learning system specifically designed for college English education is proposed, based on a cloud platform that supports real-time data access, adaptive content presentation, and flexible scalability. Although English language resources are provided by cloud platforms to facilitate scalable and flexible learning, they make use of static content as well as pre-defined responses, which cannot cater to differences between learners and changing needs. The use of cloud platforms in language learning has made English learning more accessible and sharable; however, most systems lack crucial aspects such as lack of intelligence, limited personalization, and lack of adaptive feedback. Static techniques, such as rule-driven or manually curated e-learning systems, are not able to dynamically respond to student performance or provide context knowledge. In order to solve these problems, the model utilizes a Bidirectional Encoder Representations from Transformers (BERT)-based deep learning framework, which is fine-tuned for natural language processing and immediate learner feedback. The approach incorporates student performance analytics, speech-to-text processing, and grammar checking with context awareness in order to increase engagement and competency. The deep learning model, fine-tuned using BERT for language understanding tasks, achieved a notable 99.8% accuracy and an F1-score of 98.6% compared to traditional CNN and RNN-based models. Comparative performance metrics portray the robustness, scalability, and efficiency of the model in providing a personalized and intelligent English learning experience.