A hybrid intelligent approach for the support of higher education students in literature discovery

dc.contributor.authorPrater, Ryan
dc.contributor.authorLaurenzi, Emanuele
dc.contributor.editorMartin, Andreas
dc.contributor.editorHinkelmann, Knut
dc.contributor.editorFill, Hans-Georg
dc.contributor.editorGerber, Aurona
dc.contributor.editorLenat, Doug
dc.contributor.editorStolle, Reinhard
dc.contributor.editorvan Harmelen, Frank
dc.date.accessioned2024-04-19T05:50:55Z
dc.date.available2024-04-19T05:50:55Z
dc.date.issued2022
dc.description.abstractIn this paper, we present a hybrid intelligent approach that combines knowledge engineering, machine learning, and human intervention to automatically recommend literature resources relevant for a high quality of literature discovery. The primary target group that we aim to support is higher education students in their first experiences with research works. The approach builds a knowledge graph by leveraging a logistic regression algorithm which is first parameterized and then influenced by the interventions of a supervisor and a student, respectively. Both interventions allow continuous learning based on both the supervisor’s preferences (e.g. high score of H-index) and the student’s feedback to the resulting literature resources. The creation of the hybrid intelligent approach followed the Design-Science Research methodology and is instantiated in a working prototype named PaperZen. The evaluation was conducted in two complementary ways: (1) by showing how the design requirements manifest in the prototype, and (2) with an illustrative scenario in which a corpus of a research project was taken as a source of truth. A small subset from the corpus was entered into the PaperZen and Google Scholar, independently. The resulting literature resources were compared with the corpus of a research project and show that PaperZen outperforms Google Scholar
dc.description.urihttps://ceur-ws.org/Vol-3121/
dc.eventAAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022)
dc.event.end2022-03-23
dc.event.start2022-03-21
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/43342
dc.identifier.urihttps://doi.org/10.26041/fhnw-7307
dc.language.isoen
dc.relation.ispartofProceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.spatialPalo Alto
dc.subject.ddc330 - Wirtschaft
dc.titleA hybrid intelligent approach for the support of higher education students in literature discovery
dc.type04B - Beitrag Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaftde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.openAccessCategoryDiamond
fhnw.publicationStatePublished
relation.isAuthorOfPublication1629b3c4-7073-4be7-aff7-485e4b94fb42
relation.isAuthorOfPublication4a2b6cad-6ed6-4355-a377-e408a177b079
relation.isAuthorOfPublication.latestForDiscovery4a2b6cad-6ed6-4355-a377-e408a177b079
relation.isEditorOfPublication6a3865e7-85dc-41b5-afe3-c834c56fab4e
relation.isEditorOfPublication6898bec4-c71c-491e-b5f8-2b1cba9cfa00
relation.isEditorOfPublication.latestForDiscovery6a3865e7-85dc-41b5-afe3-c834c56fab4e
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