Logo des Repositoriums

Auflistung nach Schlagwort "student performance"

1 - 2 von 2
  • Konferenzbeitrag
    Data Leakage Through Click Data in Virtual Learning Environments
    (20. Fachtagung Bildungstechnologien (DELFI), 2022) Hartmann, Johanna; Heuer, Hendrik; Breiter, Andreas
    Unsupervised machine learning techniques are increasingly used to cluster students based on their activity in virtual learning environments. It is commonly assumed that clusters formed by click data merely represent the actions of users and do not allow inferring personal information about individual users. Based on an analysis of 18,660 students and 5.56 million data points from the Open University Learning Analytics Dataset, we show that clusters trained on "raw" click data are highly correlated with personal information like student success, course specifics, and student demographics. Our analysis demonstrates that these clusters allow conclusions about demographic variables like the previous education and the affluence of the residential area. Our investigation shows that apparently, objective click data can leak private attributes. The paper discusses the implications of this for the design of virtual learning environments, especially considering the legal requirements posed by the principle of data minimization of the EU GDPR.
  • 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.