A Web-Based Peer Assessment System for Language Learning and Processing

This study designs and implements a peer assessment system for language learning to enhance interactivity and teaching effectiveness. The system integrates task management, peer assessment, grade inquiry, and reporting, creating a comprehensive online platform for teachers and students. Using a front-end and back-end separation architecture, the system employs React.js for the user interface, Node.js and Express.js for backend services, and MongoDB for data storage. Key technologies include role-based access control, real-time WebSocket communication, and natural language processing-assisted scoring. Core functions include assignment submission, random allocation, multi-dimensional scoring, and data visualization. Application analysis shows the system significantly improves student engagement and critical thinking while reducing teacher workload with a 4-semester empirical analysis with a total sample of 240 participants. This research offers practical insights into peer assessment technology in language teaching and has significant implications for promoting educational informatization and personalized learning.