Personalised course recommender: Linking learning objectives and career goals through competencies
Loading...
Authors
Author (Corporation)
Publication date
2024
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Proceedings of the AAAI 2024 Spring Symposium Series
Special issue
DOI of the original publication
Link
Series
Series number
Volume
3(1)
Issue / Number
Pages / Duration
72-81
Patent number
Publisher / Publishing institution
Stanford University
Place of publication / Event location
Stanford
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
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.
Keywords
Subject (DDC)
Event
AAAI-MAKE
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Beutling, N., & Spahic, M. (2024). Personalised course recommender: Linking learning objectives and career goals through competencies. In R. Petrick & C. Geib (Eds.), Proceedings of the AAAI 2024 Spring Symposium Series: Vol. 3(1) (pp. 72–81). Stanford University. https://doi.org/10.1609/aaaiss.v3i1.31185