Konferenzbeitrag
Full Review
Task Definition in Big Sets of Heterogeneously Structured Moodle LMS Courses
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Dokumententyp
Text/Conference Paper
Dateien
Datum
2023
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Quelle
21. Fachtagung Bildungstechnologien (DELFI)
Posterbeiträge
Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Analysing Learning Management System (LMS) log data gives insight into student learning behaviour that can help to predict performance, and as a consequence to avoid drop-out. This contribution provides an application and an adaptation of Rotelli and Monreale’s methodology [RM22] for defining tasks in a set of 10,532 online courses collected from seven universities. Unlike [RM22], we access the log data directly from the Moodle database. Even though our data set is much bigger and more heterogeneous than the one described in [RM22], we could adapt the data selection and filtering, as well as the components’ redefinition and alignment and employ their methodology to define tasks. This work is a contribution to make log data preprocessing open, replicable and more transparent.