Guiding Students Towards Successful Assessments Using Learning Analytics From Behavioral Data to Formative Feedback (2024)
Abstract
Current research shows that automated feedback positively affects students’ academic performance, satisfaction with the feedback, and self-regulated learning whilst being independent of prior academic achievements. Concurrently, it has been shown that high-information feedback has the largest effect sizes for learning outcomes and academic performance. The following chapter provides insights to an approach to provide formative feedback supported by artificial intelligence in distance learning, that does not analyze summative assessment data, but rather intends to guide students in their learning process towards the graded final assessment. Approaches that are using methods of artificial intelligence and learning analytics have in common that they need data to derivate senseful outcomes. But how can students’ behavior in online learning courses be measured, and which concrete clickstream entries can be used to calculate these measures? This contribution looks at indicators focusing on data for supporting metacognitive learning strategies and illustrates especially the process to extract measures of behavioral engagement from raw log data and its conversion into high-information feedback. The entire process is reflected in collaboration with lecturers to design a didactically guided, user-centered interface that supports student reflections towards improving their learning and assessment preparation. The pursued solution includes a dashboard in combination with a rule-based personalized feedback text, connecting engagement measures with additional information (e.g., learning material, techniques, etc.). The chapter will give insight into the interdisciplinary elaboration process of learning dashboards and scientifically based development of high-information feedback texts, beside a practical insight into data transformation for learning analytics.
Veröffentlichung
Hanses, M., van Rijn, L., Karolyi, H., de Witt, C. (2024). Guiding Students Towards Successful Assessments Using Learning Analytics From Behavioral Data to Formative Feedback. In: Sahin, M., Ifenthaler, D. (eds) Assessment Analytics in Education. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-56365-2_4
Computer-Generated formative Feedback using pre-selected Text Snippets (2023)
Veröffentlichung
Rüdian, L. S., Schumacher, C., Kuzilek, J., & Pinkwart, N. (2023). Computer-Generated formative Feedback using pre-selected Text Snippets [Poster]. The 13. International Learning Analyitcs and Knowledge Conference (LAK).
Trusted Learning Analytics verstetigen – Mit Change Management zu didaktischen Innovationen (2022)
Abstract
Bestrebungen, mit Learning Analytics universitäres Lernen und Lehren in digitalen Umgebungen besser zu verstehen und zu optimieren, führten bislang nur zu wenigen Ansätzen und Beispielen für eine systematische Implementierung von datengestützten Lernanalysen an deutschen Hochschulen. Der Prozess, Anwendungen von Learning Analytics (LA) und Künstlicher Intelligenz (KI) in die breite Nutzung an Hochschulen in Deutschland zu bringen, geht aktuell in eine neue Implementierungsstufe über. Hürden der (ressourcen-)technischen Ebene (Ifenthaler, 2017), der organisationalen (Jenert, 2020) und partizipativen Rahmungen (Mayrberger, 2019; 2020) werden durch konkrete, strukturierend begleitende Ansätze wie Trusted Learning Analytics (TLA) (Drachsler & Greller, 2016; Hansen et al., 2020) und das Sheila-Framework (Tsai et al., 2018) überwindbar. Der Posterbeitrag veranschaulicht die initialen Schritte eines solchen Implementierungsprozesses für formatives Feedback und dessen didaktische Konzeption, basierend auf dem Ansatz des hochinformativen Feedbacks nach Wisniewski et al. (2020), anhand von IMPACT.
Veröffentlichung
van Rijn, L., Karolyi, H., & de Witt, C. (2022). Trusted Learning Analytics verstetigen – Mit Change Management zu didaktischen Innovationen [Poster]. 30. Jahrestagung der Gesellschaft für Medien in der Wissenschaft e.V.