Lea Cohausz
Researcher
B6, 26, Room C1.07
E-mail: lea.cohausz (at) uni-mannheim.de
Focus group: Artificial Intelligence
Visit my website
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)
Publications
- Cohausz, L., Tschalzev, A., Bartelt, C. and Stuckenschmidt, H. (2024). Investigating demographic features and their connection to performance, predictions, and fairness in EDM models. Journal of Educational Data Mining, 16, 177–213.
- 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). Combining fairness and causal graphs to advance both. In , Fairness and Bias in AI : Proceedings of the 2nd Workshop on Fairness and Bias in AI, co-located with 27th European Conference on Artificial Intelligence (ECAI 2024) (S. 1–14). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Cohausz, L., Kappenberger, J. and Stuckenschmidt, H. (2024). What fairness metrics can really tell you: A case study in the educational domain. In , LAK'24: Proceedings of the 14th Learning Analytics and Knowledge Conference (S. 792–799). , Association for Computing Machinery: Kyoto, Japan.
- Dieing, T. I., Scheffler, M. and Cohausz, L. (2024). Enhancing chatbot-assisted study program orientation. In , Workshopband der 22. Fachtagung Bildungstechnologien (DELFI) : 09.09.-11.09.2024, Fulda, Deutschland (S. 223–232). , Gesellschaft für Informatik (GI): Bonn.
- Scheffler, M., Dieing, T. I. and Cohausz, L. (2024). Developing a personalized study program recommender. In , Workshopband der 22. Fachtagung Bildungstechnologien (DELFI) : 9.-11. September 2024, Fulda, Deutschland (S. 233–240). , Gesellschaft für Informatik (GI): Bonn.
- Wilken, N., Cohausz, L., Bartelt, C. and Stuckenschmidt, H. (2024). Fact Probability Vector Based Goal Recognition. In , 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) (S. 4254-4261). Frontiers in Artificial Intelligence and Applications, IOS Press: Amsterdam [u. a.].
- 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.