Design and Application of a Web Technologybased English Course Learning System Utilizing Hybrid Recommendation Algorithms

The rapid advancement of web technologies has revolutionized English language learning, prompting the design of an innovative English Course Learning System (ECLS). This study addresses key challenges such as lack of personalized learning, low engagement, and inefficient progress tracking in traditional systems. To overcome these, we propose a hybrid recommendation algorithm integrating Collaborative Filtering (CF), Natural Language Processing (NLP), and a BERT-based deep learning model for adaptive content generation and proficiency assessment. The system leverages the Duolingo English Test dataset alongside fluent-speech-corpus to ensure diverse linguistic inputs. The BERT model enhances contextual understanding, enabling dynamic adjustments to learning paths based on real-time student interactions. Novelty emerges through BERT-powered semantic analysis, personalized feedback generation, and automated difficulty scaling, achieving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 95.3 \%$</tex> accuracy in proficiency prediction and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 20 \%$</tex> higher engagement compared to rule-based systems. Performance is evaluated using precision, recall, F1-score, demonstrating significant improvements in retention rates and satisfaction scores. This research bridges AI and language education, offering a scalable, transformer-driven framework for future e-learning systems.