Using Learning Analytics to Identify Student Learning Profiles for Software Development Courses

dc.bibliographicCitation.bookTitleECSEE '23: Proceedings of the 5th European Conference on Software Engineering Educationeng
dc.bibliographicCitation.firstPage31
dc.bibliographicCitation.lastPage37
dc.contributor.authorSöchtig, Philipp
dc.contributor.authorApel, Sebastian
dc.contributor.authorWindisch, Hans-Michael
dc.contributor.editorMottok, Jürgen
dc.date.accessioned2023-07-13T09:21:08Z
dc.date.available2023-07-13T09:21:08Z
dc.date.issued2023
dc.description.abstractOften lecturers encounter the problem of not knowing how students use the course materials during a semester. In our approach we devised a web-based system that presents all learning materials in a digital format, allowing us to record student learning activities. The recorded usage data enabled extensive analyses of student learning behaviour which can support lecturers with improving the materials as well as understanding students’ learning material preferences and learning profiles, which can be composed by combining different usage modes depending on the material used. For the lectures we analysed, a higher success in the exam can be correlated to higher usage of the learning material according to our research data. Furthermore, student preferences regarding the form of presentation (f.e. slides over videos) could also be seen.eng
dc.description.sponsorshipStiftung Innovation in der Hochschullehre of Germany BM202-EA-1690-07540
dc.description.versionacceptedVersion
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/12418
dc.identifier.urihttps://doi.org/10.34657/11448
dc.language.isoeng
dc.publisherNew York, NY : Association for Computing Machinery
dc.relation.doihttps://doi.org/10.1145/3593663.3593679
dc.relation.isbn978-1-4503-9956-2
dc.rights.licenseEs gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
dc.subject.ddc004
dc.subject.otherLearning Analyticseng
dc.subject.otherLearning Profileseng
dc.subject.otherLearning Management Systemger
dc.titleUsing Learning Analytics to Identify Student Learning Profiles for Software Development Courses
dc.typeBookParteng
dc.typeTexteng
dcterms.eventECSEE 2023: European Conference on Software Engineering Education, Seeon/Bavaria Germany, June 19 - 21, 2023
tib.accessRightsopenAccess
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Apel_Söchtig_Windisch_ECSEE23_Paper_Learning_Analytics_Pre-Abgabe.pdf
Size:
608.91 KB
Format:
Adobe Portable Document Format
Description:
Collections