REVOLUTIONIZING E-LEARNING: PERSONALIZED LEARNING PATHS WITH LSTM AND COLLABORATIVE FILTERING

E-learning, rooted in formalized teaching methods and supported by electronic resources, has revolutionized education by integrating computers and the Internet to provide flexible, accessible learning opportunities. Initially met with skepticism due to its perceived lack of human interaction, e-learning has significantly evolved, particularly with the adoption of dynamic recommendation systems. Traditional recommendation systems often relied on algorithms like k-nearest neighbors (K-NN), which, while simple and intuitive, struggle with scalability and adaptability in handling complex, evolving user data. To address these limitations, a more advanced, dynamic e- learning system is proposed, combining Natural Language Processing (NLP) and Long Short Term Memory (LSTM) networks. NLP plays a crucial role in interpreting and understanding user queries expressed in natural language, enabling the system to comprehend the learner’s intent more effectively. Meanwhile, LSTMs, a type of Recurrent Neural Network (RNN), are capable of learning from sequential data, making them well-suited for tracking user behavior and predicting future learning needs based on past interactions. This synergy between NLP and LSTMs enables the system to dynamically adapt its content recommendations to suit individual learning preferences and goals. Unlike traditional models, the proposed system continuously learns and evolves, enhancing its predictive accuracy and user engagement over time. By leveraging these advanced technologies, the proposed e-learning model not only overcomes the constraints of earlier systems but also offers a more personalized, efficient, and engaging learning experience. This marks a significant step in the technological advancement of e-learning, aligning digital education with the needs of modern learners.