About Me

I am an external PhD student at the University of Tübingen under the supervision of Prof. Dr. Gjergji Kasneci and Prof. Dr. Benjamin Nagengast. I obtained a Bachelor’s and a Master’s degree in Psychology (University of Bamberg and Ulm University), where I focused on implementing Machine Learning and explainable AI techniques to better understand psychological processes based on Big Data. Additionally, I completed a Master’s degree in the area of applied computer science (University of Bamberg), where I investigated construct learning of deep learning models with explainable AI.

Research Interest

Currently, I am mainly interested in enriching machine learning analyses with explainability methods in education research. My main aim is an in-depth investigation of factors influencing learning success and the modeling of learning behavior. To this end, I analyze log file data from digital learning systems and multimodal behavioral data. Further, I work on generating theory-aligned behavioral indicators of learning within digital learning environments.

Publications

Can You Solve This on the First Try? – Understanding Exercise Field Performance in an Intelligent Tutoring System. by Deininger, H., Lavelle-Hill, R., Parrisius, C., Pieronczyk, I., Colling, L., Meurers, D., Trautwein, U., Nagengast, B., & Kasneci, G. International Conference on Artificial Intelligence in Education (pp. 565-576). Cham: Springer Nature Switzerland, 2023.

Enriching visual with verbal explanations for relational concepts–combining LIME with Aleph. by Rabold, J., Deininger, H., Siebers, M., & Schmid, U. Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I (pp. 180-192). Springer International Publishing, 2020.

From beliefs to intention: Mentoring as an approach to motivate female high school students to enrol in computer science studies. by Finzel, B., Deininger, H., & Schmid, U. In Proceedings of the 4th Conference on Gender & IT (pp. 251-260), 2018.