Logo des Repositoriums
 

Recommender Systems for Vocational Training and Education: Insights from Germany’s ”Innovationswettbewerb INVITE“Prog

dc.contributor.authorRashid, Sheikh Faisal
dc.contributor.authorReichow, Insa
dc.contributor.authorBlanc, Berit
dc.contributor.editorKiesler, Natalie
dc.contributor.editorSchulz, Sandra
dc.date.accessioned2024-10-21T10:40:35Z
dc.date.available2024-10-21T10:40:35Z
dc.date.issued2024
dc.description.abstractThe integration of recommender systems (RS) into digital vocational education and training (VET) programs holds significant potential for personalized learning and skill development. While most scientific studies have traditionally focused on the application of RS in higher education, this paper shifts the focus to the VET sector. It provides an overview of the development and application of RS within the context of the German funding program “INVITE”. INVITE supports 35 multi-stakeholder projects fostering innovation in digital learning platforms for VET. Out of the 35 projects, 22 develop RS tailored to different target groups and domains. The RS primarily aim to enhance learner support by recommending adaptive learning paths, personalized learning content, and further training opportunities. Based on the available documentation, this paper provides a structured analysis of the developed RS across diverse VET application domains within the INVITE program.en
dc.identifier.doi10.18420/delfi2024-ws-34
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45050
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofProceedings of DELFI Workshops 2024
dc.relation.ispartofseriesDELFI
dc.subjectRecommender Systems
dc.subjectAdaptive learning paths
dc.subjectPersonalized learning
dc.subjectVocational Education and Training
dc.subjectInnovationswettbewerb INVITE
dc.titleRecommender Systems for Vocational Training and Education: Insights from Germany’s ”Innovationswettbewerb INVITE“Progen
dc.typeText/Conference Paper
mci.conference.date09.-11. September 2024
mci.conference.locationFulda
mci.conference.sessiontitleDELFI: Workshop
mci.document.qualitydigidoc
mci.reference.pages249-252

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
05_rashid_recommender.pdf
Größe:
237.37 KB
Format:
Adobe Portable Document Format