Methodenzentrum

Dr. Pascal Kilian

Wissenschaftlicher Mitarbeiter am Methodenzentrum

Büro
Methodenzentrum
Haußerstr. 11
72076 Tübingen


 

Forschung

Schwerpunkte

  • Anwendung quantitativer Analysemethoden und Machinelles Lernen in der Sportwissenschaft (Fußball)
  • Maschinelles Lernen in den Sozial- und Verhaltenswissenschaften
  • Verknüpfung des Machinellen Lernens mit Methoden der Psychometrie (Mehrebenenmodelle, Latente Variablen Modelle)

Kurzvita

seit 12/2018
Postdoc

am Methodenzentrum, Eberhard Karls Universität Tübingen

Promotionspreis für herausragende Abschlussarbeiten

verliehen von der Universität Tübingen (Tübingen School of Education)

07/2018
Promotion (Dr. rer. nat.)

an der Mathematisch-Naturwissenschaftlichen Fakultät (Titel: On CK, PCK and Student Dropout in the Early Phase of Math (Teacher) Education at University)

03/2018 - 08/2018
Wissenschaftlicher Mitarbeiter (Doktorand) am Methodenzentrum

Universität Tübingen

03/2017 - 08/2018
Projekt "SAM"

BMBF-gefördert (Tübinger Teilprojektleitung)

10/2014 - 04/2017
Projekt "Entwicklungsverbund 3"

bei der Deutschen Telekom Stiftung (Projektleitung)

01/2014 - 03/2018
Wissenschaftlicher Mitarbeiter (Doktorand)

am Fachbereich Mathematik, Universität Tübingen

10/2009 - 12/2013
Wissenschaftliche Hilfskraft

am Fachbereich Mathematik, Universität Tübingen

10/2007 - 10/2012
Studium: Mathematik und Physik

an der Universität Tübingen

Publikationen

Auswahl an relevanten Zeitschriftenartikeln

  • Kilian, P., Leyhr, D., Höner, O., & Kelava, A. (submitted). A deep learning factor analysis model based on importance-weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players.
  • Andriamiarana, V., Kilian, P., Kelava, A., & Brandt, H. (2023). On the requirements of non-linear dynamic latent class SEM: A simulation study with varying numbers of subjects and time points. Structural Equation Modeling. Link
  • Kilian, P., Ye, S., & Kelava, A. (2023). Mixed effects in machine learning - A flexible mixedML framework to add random effects to supervised machine learning regression. Transactions on Machine Learning Research (TMLR). Link
  • Kelava, A., Kilian, P., Glaesser, J., Merk, S., & Brandt, H. (2022). Forecasting intraindividual changes of affective states taking into account interindividual differences using intensive longitudinal data from a university student drop out study in math. Psychometrika, 87(2), 533-558. Link
  • Glaesser, J., Kilian, P., & Kelava, A. (2021). Mögliche Vorläufer von Studienabbruch in der Mathematik: stabile Persönlichkeitsmerkmale und veränderliche affektive Zustände. In M. Neugebauer, H.-D. Daniel, & A. Wolter (Eds.), Studienerfolg und studienabbruch (pp. 127–149). Wiesbaden: Springer Fachmedien Wiesbaden. doi: 10.1007/978-3-658-32892-4_6
  • Kilian, P., Glaesser, J., Loose, F., & Kelava, A. (2021). Structure of pedagogical content knowledge in maths teacher education. Psychological Test and Assessment Modeling, 63, 337–360.
  • Kilian, P., Loose, F., & Kelava, A. (2020). Predicting math student success in the initial phase of college with sparse information using approaches from statistical learning. Frontiers in Education, 5. Link

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