Semi-assisted Module Handbook Content Extraction for the Application of Curriculum Analytics
| dc.contributor.author | Roepke, Rene | |
| dc.contributor.author | Nell, Maximilian | |
| dc.contributor.author | Schroeder, Ulrik | |
| dc.contributor.editor | Röpke, René | |
| dc.contributor.editor | Schroeder, Ulrik | |
| dc.date.accessioned | 2023-08-30T09:09:37Z | |
| dc.date.available | 2023-08-30T09:09:37Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Alongside examination regulations, module handbooks provide overview of a study program, including information like workload, learning goals, examinations. They provide guidance to students, but can also be a valuable information source to curriculum analytics, e.g., the identification of trends and patterns across modules, the assessment of course content coherence, and data-driven decision-making regarding curriculum design and revision. This paper introduces a tool for semi-assisted module handbook content extraction, which uses natural language processing and text mining techniques to extract all properties and relevant details from module handbooks, allowing instructors and curriculum designers to efficiently identify key information. As module handbooks between institutions may look very different, fully automated extraction is difficult and error-prone. By allowing users to verify and correct extraction results in a semi-assisted manner, higher accuracy and reliability of module data can be achieved. | en |
| dc.identifier.doi | 10.18420/delfi2023-45 | |
| dc.identifier.isbn | 978-3-88579-732-6 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/42206 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | 21. Fachtagung Bildungstechnologien (DELFI) | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-322 | |
| dc.subject | Curriculum Analytics | |
| dc.subject | Natural Language Processing | |
| dc.subject | Text Mining | |
| dc.subject | Module Handbooks | |
| dc.title | Semi-assisted Module Handbook Content Extraction for the Application of Curriculum Analytics | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 260 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 259 | |
| gi.conference.date | 11.-13. September 2023 | |
| gi.conference.location | Aachen | |
| gi.conference.review | full | |
| gi.conference.sessiontitle | Demobeiträge |
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