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Type: bookPart × Sponsor: Stiftung Innovation in der Hochschullehre of Germany BM202-EA-1690-07540 ×

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    Using Learning Analytics to Identify Student Learning Profiles for Software Development Courses
    (New York, NY : Association for Computing Machinery, 2023) Söchtig, Philipp; Apel, Sebastian; Windisch, Hans-Michael; Mottok, Jürgen
    Often 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.