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Discovering response patterns

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Research Article: Sieg, M., Roselló Atanet, I., Tomova, M.T. et al. Discovering unknown response patterns in progress test data to improve the estimation of student performance. BMC Med Educ 23, 193 (2023).

Conference paper: Gemeinsame Jahrestagung der Gesellschaft für Medizinische Ausbildung (GMA) und des Arbeitskreises zur Weiterentwicklung der Lehre in der Zahnmedizin (AKWLZ).2022.Halle/Saale.

2022 Institut of Biometry und Clinical Epidemiology, Charité - Universitätsmedizin Berlin
2021 Kooperationstreffen AG PTM

Unsupervised Learning - unüberwachtes Lernen

Unsupervised learning uses machine learning algorithms to analyze and group uncategorized or unlabeled data sets. These algorithms discover underlying structures, hidden patterns, or data groupings without requiring human intervention. They can be divided into clustering and association algorithms:

  • Clustering elicits inherent groupings in the data, e.g., grouping customers by their buying behavior - or students by their answers
  • Association elicits rules or correlations, e.g., that people who buy X tend to buy Y.

Because these algorithms discover similarities and differences in information, they are used for exploratory data analysis, customer segmentation, among others.