Automated Analysis of Learning Outcomes and Exam Questions Based on Bloom's Taxonomy

- The study focuses on the automatic classification of exam questions and learning outcomes using Bloom's Taxonomy, processing a dataset of 600 sentences across six cognitive categories. This research highlights the effectiveness of machine learning models, particularly Support Vector Machines, which achieved a notable 94% accuracy, indicating a significant advancement in educational assessment methodologies. The findings underscore the challenges of training complex models on limited data, reflecting a broader trend in AI research towards optimizing performance while managing overfitting.