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

This paper develops and evaluates automated classification systems for exam questions and learning outcomes according to Bloom's Taxonomy cognitive levels, comparing traditional ML models, RNNs, transformers (BERT/RoBERTa), and LLMs on a 600-sentence dataset. The best performance was achieved by augmented SVM (94% accuracy), while LLMs achieved 72-73% accuracy in zero-shot settings.

This paper explores the automatic classification of exam questions and learning outcomes according to Bloom's Taxonomy. A small dataset of 600 sentences labeled with six cognitive categories - Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation - was processed using traditional machine learning (ML) models (Naive Bayes, Logistic Regression, Support Vector Machines), recurrent neural network architectures (LSTM, BiLSTM, GRU, BiGRU), transformer-based models (BERT and RoBERT

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