Developing Autonomous Feedback Mechanisms in Education with Deep Q-networks (DQN) and NLP

Abstract

The increasing deployment of digital learning environments has highlighted key shortcomings in existing static and rule-based approaches to formative feedback that do not account for the dynamic nature of learner experience. This paper proposes a novel autonomous system for personalized feedback based on a combination of Deep Q-Networks and Natural Language Processing techniques. In particular, the feedback process is modeled as an agent-based approach to reinforcement learning with a DQN-based agent that learns how to provide optimal formative feedback based on student actions. This is achieved through the construction of state-action-state transition dynamics for the Markov Decision Process using data extracted from the student interaction text using the pipeline of tokenization, sentiment analysis, named entity recognition, and intent classification. The effectiveness of the proposed approach was evaluated on a specially designed dataset of 12,847 student interactions covering three STEM fields (Mathematics, Physics, and Computer Science) collected from an online learning platform over 18 months. The results show that the proposed DQN-NLP method attains 93.7% accuracy, 91.4% precision, 92.8% recall, and 92.1% F1 score, which significantly surpasses the baselines such as rule-based feedback (F1 score = 71.3%), vanilla RL without NLP (F1 score = 82.6%), and transformers alone (F1 score = 88.4%). More importantly, the model increased the learning gain of students by 34.2% and shortened the feedback response time by 61% relative to instructor-driven feedback. The ablation experiment reveals that the contribution from DQN is 9.5%, whereas NLP adds another 7.8%. This research highlights the revolutionary impact of integrating reinforcement learning with natural language processing in education for scalable, adaptive educational feedback.