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The paper introduces a hybrid deep learning model, n-gram-LMS-MC-DL, for predicting student learning states within an e-learning system by integrating n-gram language modeling, Markov Chains, and deep learning. This approach leverages student interaction data within a Learning Management System (LMS) to forecast their next learning activity. The proposed model achieves a prediction accuracy of 92.82%, outperforming standalone Markov Chain and Deep Learning models, demonstrating its effectiveness in enhancing personalized education.
A hybrid NLP model can predict student learning states with 92.82% accuracy, significantly outperforming traditional deep learning and Markov Chain approaches in e-learning systems.
Natural Language Processing (NLP) with deep learning transforms how machines understand and generate human language by leveraging powerful neural networks, such as Recurrent Neural Networks (RNNs) and Transformers. Deep learning models in NLP can process vast amounts of unstructured text data, enabling highly accurate tasks like sentiment analysis, machine translation, text summarization, and question-answering. Course construction refers to designing, organizing, and implementing educational content to achieve specific learning objectives. A Learning Management System (LMS) integrated with deep learning in e-learning revolutionizes personalized education by analyzing student data to provide customized learning paths and experiences. Deep learning models in the LMS can assess a student's learning style, pace, and areas of difficulty by processing large volumes of user interactions, such as quizzes, assignments, and engagement metrics. The proposed n-gram-LMS-MC-DL model integrates n-gram language modeling, Markov Chain, and Deep Learning (DL) techniques to enhance the prediction accuracy in Learning Management Systems (LMS). This hybrid approach aims to predict students' next learning states based on their interactions within the LMS. The system achieves significant improvements in prediction accuracy across various learning stages. For instance, the n-gram-LMS-MC-DL model outperforms both the standalone Markov Chain and Deep Learning models, reaching an average accuracy of 92.82%, compared to 85.52% for Deep Learning and 77.78% for the Markov Chain. In individual stages, the model predicts with 92.1% accuracy for transitioning from “Lesson Completed” to “Quiz Started” and 95.2% accuracy for progressing from “New Lesson” to “Discussion.” In addition to enhanced accuracy, the system maintains high precision (average 0.87), recall (average 0.85), and F1-score (average 0.855) across various learning activities, with manageable time complexities ranging from 120 ms to 155 ms.
