Personalised Course Recommender: Linking Learning Objectives and Career Goals through Competencies


  • Nils Beutling FHNW University of Applied Sciences and Arts Northwestern Switzerland
  • Maja Spahic-Bogdanovic FHNW University of Applied Sciences and Arts Northwestern Switzerland University of Camerino (UNICAM)



Large Language Models, Recommender Systems, Higher Education, Knowledge-Based Recommender System


This paper presents a Knowledge-Based Recommender System (KBRS) that aims to align course recommendations with students' career goals in the field of information systems. The developed KBRS uses the European Skills, Competences, qualifications, and Occupations (ESCO) ontology, course descriptions, and a Large Language Model (LLM) such as ChatGPT 3.5 to bridge course content with the skills required for specific careers in information systems. In this context, no reference is made to the previous behavior of students. The system links course content to the skills required for different careers, adapts to students' changing interests, and provides clear reasoning for the courses proposed. An LLM is used to extract learning objectives from course descriptions and to map the promoted competency. The system evaluates the degree of relevance of courses based on the number of job-related skills supported by the learning objectives. This recommendation is supported by information that facilitates decision-making. The paper describes the system's development, methodology and evaluation and highlights its flexibility, user orientation and adaptability. It also discusses the challenges that arose during the development and evaluation of the system.






Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge