This paper presents a study of the learning guides used at the Universidad Politécnica de Madrid (UPM), with a focus on enhancing their clarity, precision, and interoperability. Learning guides typically include several sections to help students understand what the course covers, how it will be taught, and what is expected of them. In UPM learning guides, the skills and competences that are to be learned during the course are listed apart from the learning outcomes, i.e., what students are expected to know or be able to do by the end of the course. This allows us to use the information in different manners.

First, we describe the development of an algorithm that automatically analyses course learning outcomes to determine their cognitive level according to Bloom’s taxonomy. Bloom’s taxonomy is a hierarchical framework that classifies learning objectives into six levels of cognitive complexity, expressed as action verbs that reflect several stages of understanding and thinking. The six stages arranged from the most basic to the most advanced, are remember, understand, apply, analyze, evaluate, and create. The verbs included in the taxonomy are widely employed by educators when defining the learning outcomes of university courses.

Second, we argue that aligning the skills and competences described in the guides with the European ESCO (European Skills, Competences, Qualifications and Occupations) classification enables a more accurate and interoperable representation of educational content. Moreover, ESCO skills and competences are related to occupations, which can also provide an overview of how aligned courses are with the demands of the labour market.

We present preliminary results supporting this claim, including an online web portal, and discuss the possible connection to the data at O*NET (Occupational Information Network), the equivalent U.S. database for occupations description. Finally, and beyond details, we discuss the potential of adopting machine-readable formats for learning guides, leveraging international standards and vocabularies to improve transparency, comparability, and automated processing in higher education.

Keywords: Learning Guides, Bloom's taxonomy, ESCO, O*NET, Artificial Intelligence, Machine Learning, Semantic Web.