A Comparative Analysis of Different Recommender Systems for University Major and Career Domain Guidance

Autor/inn/en	Lahoud, Christine; Moussa, Sherin; Obeid, Charbel; El Khoury, Hicham; Champin, Pierre-Antoine
Titel	A Comparative Analysis of Different Recommender Systems for University Major and Career Domain Guidance
Quelle	In: Education and Information Technologies, 28 (2023) 7, S.8733-8759 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Moussa, Sherin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11541-3
Schlagwörter	Academic Advising; Artificial Intelligence; Majors (Students); Career Guidance; High School Students; Foreign Countries; Efficiency; Program Effectiveness; Decision Making; Lebanon + Suchen Sie Ihr Suchwort? Akademischer Rat; Künstliche Intelligenz; Berufsorientierung; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Ausland; Effectiveness; Effektivität; Wirkungsgrad; Decision-making; Entscheidungsfindung; Libanon
Abstract	Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01