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

This paper develops and compares multiple machine learning approaches (traditional ML, RNNs, transformers, and LLMs) to automatically classify exam questions and learning outcomes according to Bloom's Taxonomy cognitive levels using a dataset of 600 labeled sentences. The study finds that augmented Support Vector Machines achieve the best performance (94% accuracy) while deep learning models suffer from overfitting on the small dataset.

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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