Investigating Methods for Mapping Learning Objectives to Bloom's Revised Taxonomy in Course Descriptions for Higher Education

Abstract

Aligning Learning Objectives (LOs) in course descriptions with educational frameworks such as Bloom’s revised taxonomy is an important step in maintaining educational quality, yet it remains a challenging and often manual task. With the growing availability of large language models (LLMs), a natural question arises: can these models meaningfully automate LO classification, or are non-LLM methods still sufficient? In this work, we systematically compare LLM- and non-LLM-based methods for mapping LOs to Bloom’s taxonomy levels, using expert annotations as the gold standard. LLM-based methods consistently outperform non-LLM methods and offer more balanced distributions across taxonomy levels. Moreover, contrary to common concerns, we do not observe significant biases (e.g. verbosity or positional) or notable sensitivity to prompt structure in LLM outputs. Our results suggest that a more consistent and precise formulation of LOs, along with improved methods, could support both automated and expert-driven efforts to better align LOs with taxonomy levels.

Alessandra Zarcone

Professor of Language Technologies and Cognitive Assistants

Computational linguist with a background in NLP and in psycholinguistics, working on AI, NLP and human-machine interaction.