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Auflistung nach Autor:in "Wiskott, Laurenz"

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  • Konferenzbeitrag
    Ein Dashboard für die Studienberatung: Technische Infrastruktur und Studienverlaufsplanung im Projekt KI:edu.nrw
    (Workshops der 21. Fachtagung Bildungstechnologien (DELFI), 2023) Baucks, Frederik; Leschke, Jonas; Metzger, Christian; Wiskott, Laurenz
    In diesem Beitrag präsentieren wir die Entwicklung und ein erster Prototyp für ein Learning Analytics-Dashboard zur Studienverlaufsplanung in der Studienberatung. Das Dashboard wird im Rahmen des interdisziplinären Projekts KI:edu.nrw entwickelt, mit dem Ziel, die Studienberatung zu verbessern und einen individuellen Studienverlauf zu fördern. Unser Beitrag konzentriert sich auf die Vorstellung der technischen Infrastruktur sowie deren Anwendung in dem speziellen Dashboard.
  • Konferenzbeitrag
    Mitigating Biases using an Additive Grade Point Model: Towards Trustworthy Curriculum Analytics Measures
    (21. Fachtagung Bildungstechnologien (DELFI), 2023) Baucks, Frederik; Wiskott, Laurenz
    Curriculum Analytics (CA) tries to improve degree program quality and learning experience by studying curriculum structure and student data. In particular, descriptive data measures (e.g., correlation-based curriculum graphs) are essential to monitor whether the learning process proceeds as intended. Therefore, identifying confounders and resulting biases and mitigating them should be critical to ensure reliable and fair results. Still, CA approaches often use raw student data without considering the influence of possible confounders such as student performance, course difficulty, workload, and time, which can lead to biased results. In this paper, we use an additive grade model to estimate these confounders and verify the validity and reliability of the estimates. Further, we mitigate the estimated confounders and investigate their impact on the CA measures course-to-course correlation and order benefit. Using data from 574 Computer Science Bachelor students, we show that these measures are significantly confounded and mislead to biased interpretations.
  • Zeitschriftenartikel
    Slow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Escalante-B., Alberto N.; Wiskott, Laurenz
    Slow Feature Analysis (SFA) is an unsupervised learning algorithm based on the slowness principle and has originally been developed to learn invariances in a model of the primate visual system. Although developed for computational neuroscience, SFA has turned out to be a versatile algorithm also for technical applications since it can be used for feature extraction, dimensionality reduction, and invariance learning. With minor adaptations SFA can also be applied to supervised learning problems such as classification and regression. In this work, we review several illustrative examples of possible applications including the estimation of driving forces, nonlinear blind source separation, traffic sign recognition, and face processing.