Design of an Intelligent Q&A System for Online Education Platform Based on Natural Language Processing Technology

Abstract

An intelligent Q&A system for online education platforms leverages natural language processing (NLP) technology to understand and respond to user queries effectively. By analysing the structure and context of questions, the system can provide accurate and relevant answers from a vast repository of educational content. Through techniques like named entity recognition, sentiment analysis, and semantic understanding, it can interpret complex queries and deliver personalized responses tailored to the user's needs. This paper explores the integration of natural language processing (NLP) technology into online education platforms to enhance teaching and learning experiences. The proposed model uses the Latent Dirichlet Allocation (LDA) with the weighted factor for the estimation of features in the NLP process. The proposed LDA model estimates the weights in the model and evaluates the Q & A in online teaching. Through intelligent Q&A systems, topic modeling, text summarization, sentiment analysis, language translation, content recommendation, automated grading, and adaptive learning, NLP offers a range of functionalities that personalize and optimize the educational journey. The processed model is evaluated with the Recurrent Neural Network (RNN) for the classification of features in the NLP system for the Intelligent Q & A system. The findings reveal significant improvements in engagement levels, with an average increase of 30% observed across different platforms. Additionally, accessibility is enhanced, as demonstrated by a 40% reduction in barriers faced by students with disabilities. Efficiency gains are evident, with a 50% decrease in time required for grading assignments and providing feedback. Moreover, effectiveness is demonstrated by a 25% improvement in student performance metrics, including exam scores and course completion rates.