392124 Tutorial B: Introduction to Data mining (Ü) (WiSe 2024/2025)

Der Kurs deckt grundlegende Methoden des data mining, der explorativen Datenanalyse und der Visualisierung ab. Der Fokus liegt auf Anwendungen auf Bildungsdaten (sog. 'educational data mining' oder 'learning analytics'). Beispielmethoden sind:

• statistische Tests, insb. t-tests und signed rank test
• Principal Component Analysis
• k-Means Clustering
• Item Response Theory
• Bayesian Knowledge Tracing
• Variational Auto Encoders

ENGLISH VERSION:

The course covers foundational methods of data mining, explorative data analysis, and visualization. The focus is on educational data (so-called 'educational data mining' or 'learning analytics'). Example methods are:

• statistical tests, esp. t-tests and signed rank test
• Principal Component Analysis
• k-Means Clustering
• Item Response Theory
• Bayesian Knowledge Tracing
• Variational Auto Encoders

Teilnahmevoraussetzungen, notwendige Vorkenntnisse

Nützlich: Neuronale Netze und Lernen, Bildverarbeitung, Vertiefung Mathematik
Querbezüge zu: Information Visualization, Introduction to Machine Learning, Mustererkennung bzw. Musterklassifikation

Literaturangaben

• Baker, Yacef (2009): The State of Educational Data Mining in 2009: A Review and Future Visions. https://jedm.educationaldatamining.org/index.php/JEDM/article/view/8
• Abdi, Williams (2010): Principal component analysis. https://doi.org/10.1002/wics.101
• Jain (2010): Data clustering: 50 years beyond K-means. https://doi.org/10.1016/j.patrec.2009.09.011
• Baker (2001): The basics of item response theory. https://files.eric.ed.gov/fulltext/ED458219.pdf
• Abdelrahman, Wang, Nunes (2022): Knowledge Tracing: A Survey. https://doi.org/10.1145/3569576
• Paaßen, Dywel, Fleckenstein, Pinkwart (2022): Sparse Factor Autoencoders for Item Response Theory. https://files.eric.ed.gov/fulltext/ED624123.pdf