Authors: Anjali Kapoor, Dr Mridula
Abstract: In order to facilitate dynamic customization in contemporary learning environments, this study introduces a Smart Autonomous System for Adaptive Student Profiling. To create constantly changing student profiles, the suggested architecture incorporates a variety of educational data sources, such as academic achievement, behavioral patterns, cognitive traits, environmental data, social interactions, and emotional indications. To handle missing data, the system uses KNN-based imputation Convolutional Neural Networks (CNNs) for emotion identification, Principal Component Analysis (PCA) for feature reduction, and XG Boost for academic risk prediction and student profile. A Deep Q-Network (DQN)-based reinforcement learning method that modifies suggestions and interventions based on learner needs enables autonomous decision-making. A hybrid recommendation engine also facilitates optimum learning paths and individualized material distribution. Real-time profiling, ongoing monitoring, proactive intervention, and adaptive feedback mechanisms are made possible by the framework's implementation within a multi-agent architecture. Learner engagement, academic achievement, and early detection of at-risk kids have all improved, according to experimental evaluation utilizing benchmark educational datasets. The findings show that the suggested approach is reliable, scalable, and successful in facilitating intelligent, customized learning environments.
