Enhancing Personalized Feedback in Adaptive E-Learning Systems: A Neural Network Approach

This study presents a novel approach to enhancing personalized feedback in e-learning platforms through the use of machine learning algorithms. As part of the research process, we compile and examine anonymized student data to develop a model capable of generating personalized text feedback. The model is trained and validated using a comprehensive dataset sourced from various educational resources. In addition, a Streamlit web application has been developed to allow users to provide personalized feedback. An iterative refinement process enhances the model's functionality and intuitiveness. An evaluation of the relevance and quality of the generated feedback is carried out through a manual assessment and the collection of user feedback. The study demonstrates significant contributions to improving student learning outcomes through personalized assistance. This study provides a significant advancement in the field of e-learning by empowering students to take ownership of their education through personalized feedback tailored to their individual needs.