Better Safe than Sorry: Visualizing, Predicting, and Successfully Guiding Courses of Study
| dc.contributor.author | Kerth, Alexander | |
| dc.contributor.author | Schuhknecht, Felix | |
| dc.contributor.author | Pensel, Lukas | |
| dc.contributor.author | Henneberg, Justus | |
| dc.contributor.editor | König-Ries, Birgitta | |
| dc.contributor.editor | Scherzinger, Stefanie | |
| dc.contributor.editor | Lehner, Wolfgang | |
| dc.contributor.editor | Vossen, Gottfried | |
| dc.date.accessioned | 2023-02-23T13:59:57Z | |
| dc.date.available | 2023-02-23T13:59:57Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Successfully going through a course of study is a lengthy and challenging task. To obtain a degree, many obstacles must be overcome and the right decisions must be made at the right point in time, often overwhelming students. To reduce the amount of dropouts, the goal of study advisors is to reach out to endangered students in time and to provide them help and guidance. To support the work of study advisors, who typically have to monitor a large amount of students simultaneously, we present in this demonstration an easy-to-use graphical tool that (a) allows the advisor to visualize all relevant information of study data in a responsive graph in order to overview the current study situation. Additional to visualization, our tool provides (b) a forecasting functionality based on pre-trained models and (c) a warning feature to identify endangered students early on. In the on-site demonstration, the audience will be able to step into the role of a study advisor and use our tool and all of its features to identify and guide struggling students within anonymized real-world study data. | en |
| dc.identifier.doi | 10.18420/BTW2023-36 | |
| dc.identifier.isbn | 978-3-88579-725-8 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40342 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BTW 2023 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-331 | |
| dc.subject | Study monitoring | |
| dc.subject | Study Prediction | |
| dc.subject | Visualization | |
| dc.subject | Machine Learning | |
| dc.subject | Graph Databases | |
| dc.title | Better Safe than Sorry: Visualizing, Predicting, and Successfully Guiding Courses of Study | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 671 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 665 | |
| gi.conference.date | 06.-10. März 2023 | |
| gi.conference.location | Dresden, Germany |
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