Personalised course recommender: Linking learning objectives and career goals through competencies
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2024
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Proceedings of the AAAI 2024 Spring Symposium Series
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
3(1)
Ausgabe / Nummer
Seiten / Dauer
72-81
Patentnummer
Verlag / Herausgebende Institution
Stanford University
Verlagsort / Veranstaltungsort
Stanford
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
AAAI-MAKE
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
BEUTLING, Nils und Maja SPAHIC, 2024. Personalised course recommender: Linking learning objectives and career goals through competencies. In: Ron PETRICK und Christopher GEIB (Hrsg.), Proceedings of the AAAI 2024 Spring Symposium Series. Stanford: Stanford University. 2024. S. 72–81. Verfügbar unter: https://irf.fhnw.ch/handle/11654/48407