Developing a personalized study program recommender


Scheffler, Marc ; Dieing, Thilo I. ; Cohausz, Lea


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DOI: https://doi.org/10.18420/delfi2024-ws-33
URL: https://dl.gi.de/items/4e7722e6-5263-48df-be62-edf...
URN: urn:nbn:de:bsz:180-madoc-678563
Document Type: Conference or workshop publication
Year of publication: 2024
Book title: Workshopband der 22. Fachtagung Bildungstechnologien (DELFI) : 9.-11. September 2024, Fulda, Deutschland
Page range: 233-240
Conference title: EduRS 2024 - Recommender Systems in Education, Workshop bei der DELFI 2024, 23. Fachtagung Bildungstechnologien (DELFI)
Location of the conference venue: Fulda, Germany
Date of the conference: 09.09.24
Publisher: Kiesler, Natalie ; Schulz, Sandra
Place of publication: Bonn
Publishing house: Gesellschaft für Informatik (GI)
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Keywords (English): education , recommender system , study program recommendation , NLP , fastText , embed- dings , BERUFENET
Abstract: This paper presents a recommender system designed to match prospective students with study programs in Baden-Württemberg, Germany, streamlining the selection process by providing personalized recommendations based on user queries. Utilizing data from approximately 1,500 study programs and employing natural language processing and machine learning techniques, specifically the German fastText model for word embeddings, our system captures the semantic relationships between user queries and program descriptions. We evaluated the system’s performance using both manual test cases and automated validation methods. The manual evaluation involved subjective assessments by multiple raters, while the automated approach utilized self-supervised keyword-based approaches. The results demonstrate the system’s effectiveness in enhancing the study program selection process.


SDG 4: Quality Education


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