Design and research of automated teaching feedback and student performance assessment system based on NLP

Abstract

With the intelligent development of the education industry, traditional teaching evaluation and feedback methods can no longer meet personalized and diversified learning needs. The automated teaching feedback and student performance evaluation system based on natural language processing (NLP) technology can automatically generate personalized and accurate teaching feedback by comprehensively analyzing students’ learning behaviors and improving educational effects. In this study, a system based on NLP technology is designed and implemented, which evaluates students’ learning status and generates feedback by analyzing multi-modal information such as students’ homework, online discussion, and emotional data. Specifically, the system combines deep learning models, sentiment analysis, and semantic understanding technologies, which can capture students’ learning progress, emotional state, and cognitive level in real-time and provide dynamic learning assessment and feedback suggestions for teachers and students. The results show that the system can enhance students’ learning motivation by 88.65% in discussions and assignments, improve knowledge retention as 65.9% of students demonstrated better course content comprehension, and optimize teacher-student interaction efficiency—with 91.3% of students reporting that personalized suggestions significantly aided their progress. Specifically, real-time emotional analysis (identifying 77.8% of students with above-average emotional engagement) correlates with sustained learning attitude in 45.5% of students, while dynamic feedback reduces delays in intervention, indirectly lowering potential dropout risks.