Lea Cohausz

Researcher

B6, 26, Room C1.07

E-mail: lea.cohausz (at) uni-mannheim.de

Focus group: Artificial Intelligence

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Research Interests

Causal Modelling (causal/BN structure learning)
Fairness, Algorithmic Bias
Goal Recognition, Plan Recognition

Teaching

Industrial Applications of AI (Spring 2023 / Spring 2022, M.Sc. Business Informatics, M.Sc. Data Science)
Decision Support (Fall 2023 / Fall 2022 / Fall 2021, M.Sc. Business Informatics, M.Sc. Data Science)
Seminar Content Recommendation (Fall 2023)
Team Project (Spring/Fall 2023)

Alturki, S., Cohausz, L. and Stuckenschmidt, H. (2022). Predicting master’s students’ academic performance: An empirical study in Germany. Smart Learning Environments, 9, 1–22.
Cohausz, L. (2022). When probabilities are not enough – A framework for causal explanations of student success models. Journal of Educational Data Mining, 14, 52–75.

Cohausz, L., Kappenberger, J. and Stuckenschmidt, H. (2024). What fairness metrics can really tell you: A case study in the educational domain. In , LAK2024: 14th International Learning Analytics and Knowledge Conference (S. 24–30). , Association for Computing Machinery: Kyoto, Japan.
Cohausz, L., Tschalzev, A., Bartelt, C. and Stuckenschmidt, H. (2023). Investigating the importance of demographic features for EDM-predictions. In , Proceedings of the 16th International Conference on Educational Data Mining (S. 125–136). , International Educational Data Mining Society: Bengaluru, India.
Wilken, N., Cohausz, L., Bartelt, C. and Stuckenschmidt, H. (2023). Planning landmark based goal recognition revisited: Does using initial state landmarks make sense? In , KI 2023: Advances in Artificial Intelligence : 46th Conference on AI, Berlin, Germany, September 26–29, 20023, proceedings (S. 231–244). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Cohausz, L. (2022). Towards real interpretability of student success prediction combining methods of XAI and social science. In , Proceedings of the 15th International Conference on Educational Data Mining (S. 361–367). , International Educational Data Mining Society: Durham.
Cohausz, L., Wilken, N. and Stuckenschmidt, H. (2022). Plan-similarity based heuristics for goal recognition. In , 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) : PerCom Workshops 2022 (S. 316–321). , IEEE: Pisa.
Wilken, N., Cohausz, L., Schaum, J., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2022). Leveraging planning landmarks for hybrid online goal recognition. In , International Conference on Automated Planning and Scheduling ICAPS (2022) : June 13–17, 2022, virtual (S. ). , CEUR Workshop Proceedings: Aachen.