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Auflistung nach Schlagwort "Learning analytics"

1 - 2 von 2
  • Konferenzbeitrag
    Enhancing educational insights: A real-time data analytics stack for project-basedlearning
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Gücük, Gian-Luca; Simic, Dejan; Leible, Stephan; Lewandowski, Tom; Kučević, Emir
    This paper presents a real-time data analytics (DA) stack designed for a project-based course utilizing Jira for project management at a university. The DA stack follows an Extract, Transform, and Load process to visualize students’ usage data within dashboards. The DA stack supports course management by providing insights into students’ activities and progress. We demonstrate the DA stack’s effectiveness through an evaluative case study, which was found to support course objectives and foster improved behavioral adaptations from lecturers to students. Furthermore, we propose a generic DA stack for generalizing and adopting it for similar applications, considering the extensibility and maintainability inherent in the open-source tools used. Moreover, we provide the GitHub repository to view our source code. This study contributes to the relatively underexplored field of real-time learning analytics and offers a starting point for the customization and adoption of the proposed DA stack in different educational contexts.
  • Konferenzbeitrag
    Student Success Prediction and the Trade-Off between Big Data and Data Minimization
    (DeLFI 2018 - Die 16. E-Learning Fachtagung Informatik, 2018) Heuer, Hendrik; Breiter, Andreas
    This paper explores student’s daily activity in a virtual learning environment in the anonymized Open University Learning Analytics Dataset (OULAD). We show that the daily activity of students can be used to predict their success, i.e. whether they pass or fail a course, with high accuracy. This is important since daily activity can be easily obtained and anonymized. To support this, we show that the binary information whether a student was active on a given day has similar predictive power as a combination of the exact number of clicks on the given day and sensitive private data like gender, disability, and highest educational level. We further show that the anonymized activity data can be used to group students. We identify different student types based on their daily binarized activity and outline how educators and system developers can utilize this to address different learning types. Our primary stakeholders are designers and developers of learning analytics systems as well as those who commission such systems. We discuss the privacy and design implications of our findings for data mining in educational contexts against the background of the principle of data minimization and the General Data Protection Regulation (GDPR) of the European Union.