Tanja Käser
EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
1015 Lausanne
+41 21 693 91 12
Office:
INF 234
EPFL
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EDIC-ENS
EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
1015 Lausanne
Web site: Web site: https://ssc.epfl.ch
EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
1015 Lausanne
Web site: Web site: https://sin.epfl.ch
Biography
Tanja Käser is an assistant professor at the EPFL School of Computer and Communication Sciences (IC) and head of the D-VET laboratory. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning.Prior to joining EPFL, Tanja Käser was a senior data scientist with the Swiss Data Science Center at ETH Zurich. Before that, she was a postdoctoral researcher with the AAALab at the Graduate School of Education of Stanford University.
Tanja Käser received her PhD degree from the Computer Science Department of ETH Zurich. In her dissertation, completed at the Computer Graphics Laboratory, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award 2015.
Publications
Infoscience publications
2023
Simulated Learners in Educational Technology: A Systematic Literature Review and a Turing-like Test
International Journal Of Artificial Intelligence In Education. 2023-07-20. DOI : 10.1007/s40593-023-00337-2.Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration
2023-04-19. CHI 2023, Hamburg, Germany, April 3-28, 2023. p. Article No.: 136, pp 1-9. DOI : 10.1145/3544549.3585644.Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design
2023-03-13. LAK 2023: The 13th International Learning Analytics and Knowledge Conference, Arlington, Texas, USA, March 13-17, 2023. DOI : 10.1145/3576050.3576147.MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
2023. 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, US, December 10-16, 2023. DOI : 10.48550/arxiv.2309.14118.How Close are Predictive Models to Teachers in Detecting Learners at Risk?
2023-01-01. 31st ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP), Limassol, CYPRUS, Jun 26-30, 2023. p. 135-145. DOI : 10.1145/3565472.3595620.The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
2023. DOI : 10.48550/arxiv.2307.00364.Co-Designing a Teacher Tool for Visualizing Self-Regulated Learning Behaviors
20232022
Improving Students Argumentation Learning with Adaptive Self-Evaluation Nudging
Proceedings of the ACM on Human-Computer Interaction. 2022-11-11. DOI : 10.1145/3555633.Maschinelles Lernen zur Förderung von höheren Kompetenzen
Lernen und Lernstörungen. 2022-10-27. DOI : 10.1024/2235-0977/a000393.Evolutionary Clustering of Apprentices' Self- Regulated Learning Behavior in Learning Journals
IEEE Transactions on Learning Technologies. 2022-08-02. DOI : 10.1109/TLT.2022.3195881.Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms
2022-07-27. 23rd International Conference on Artificial Intelligence in Education (AIED 2022), Durkham, UK, July 27-31, 2022. p. 90-102. DOI : 10.1007/978-3-031-11644-5_8.Generalisable Methods for Early Prediction in Interactive Simulations for Education
2022-07-24. 5th International Conference on Educational Data Mining, Durham, UK, July 24-27. DOI : 10.5281/zenodo.6852967.Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
2022-07-24. 15th International Conference on Educational Data Mining (EDM 2022), Durham, UK, July 24-27, 2022. DOI : 10.5281/zenodo.6852963.Meta Transfer Learning for Early Success Prediction in MOOCs
2022-05-31. 9th ACM Conference on Learning at Scale, New York, USA, June 1-3, 2022. DOI : 10.1145/3491140.3528273.Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling
2022Ripple: Concept-Based Interpretation for Raw Time Series Models in Education
2022. AAAI 2023: 37th AAAI Conference on Artificial Intelligence (EAAI: AI for Education Special Track), Washington DC, USA, February 7-14, 2023. DOI : 10.48550/arxiv.2212.01133.Introducing Productive Engagement for Social Robots Supporting Learning
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9781.Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling
2022. 29th International Conference on Computational Linguistics (COLING 2022), Gyeongju, Republic of Korea, October 12-17, 2022. DOI : 10.48550/arxiv.2209.10335.How We Use Wikipedia: Studying Readers' Behavior with Navigation Traces
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8187.Design and evaluation of digital tools to expand experience in vocational education
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8566.2021
Can Feature Predictive Power Generalize? Benchmarking Early Predictors of Student Success across Flipped and Online Courses
2021-07-02. 14th International Conference on Educational Data Mining (EDM 2021), (Online from) Paris, France, June 29th - July 2nd, 2021. p. 150-160.Early Prediction of Conceptual Understanding in Interactive Simulations
2021-06-29. 14th International Conference on Educational Data Mining, (Online) Paris, France, June 29th - July 2nd, 2021. p. 161-171.Designing Intelligent Systems for Online Education: Open Challenges and Future Directions
2021-03-12. 1st / 14th International Workshop on Enabling Data-Driven Decisions from Learning on the Web co-located with the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021), (Online) Jerusalem, Israel, March 8-12, 2021. p. 57-64.L2D 2021: First International Workshop on Enabling Data-Driven Decisions from Learning on the Web
2021-03-08. 14th ACM International Conference on Web Search and Data Mining, (Online) Jerusalem, Israel, March 8-12, 2021. p. 1165–1166. DOI : 10.1145/3437963.3441840.Computational Analysis and Design of Structurally Stable Assemblies with Rigid Parts
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-8964.Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-8037.2020
Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments
International Journal Of Artificial Intelligence In Education. 2020-09-09. DOI : 10.1007/s40593-020-00199-y.Efficacy of a Computer-Based Learning Program in Children With Developmental Dyscalculia. What Influences Individual Responsiveness?
Frontiers In Psychology. 2020-07-15. DOI : 10.3389/fpsyg.2020.01115.Towards the alignment of educational robotics learning systems with classroom activities
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-9563.Analysis and Remediation of Handwriting difficulties
Lausanne, EPFL, 2020. DOI : 10.5075/epfl-thesis-8062.2019
Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments
2019. 12th International Conference on Educational Data Mining, Montreal, Canada, July 2-5, 2019. p. 109-118.2018
Ten Years of Research on Intelligent Educational Games for Learning Spelling and Mathematics
2018-06-07Perspectives to Technology-Enhanced Learning and Teaching in Mathematical Learning Difficulties
International Handbook of Mathematical Learning Difficulties; Cham: Springer International Publishing, 2018. p. 733-754.2017
Efficient Feature Embeddings for Student Classification with Variational Auto-encoders
2017-06-25. 10th International Conference on Educational Data Mining (EDM 2017), Wuhan, China, June 25-28, 2017. p. 72-79.Effekte des Calcularis-Trainings: Teil 1: Domänen-spezifische Veränderungen
Lernen und Lernstörungen. 2017-04-01. DOI : 10.1024/2235-0977/a000166.Effekte des Calcularis-Trainings: Teil 2: Veränderungen psychosozialer Merkmale
Lernen und Lernstörungen. 2017-04-01. DOI : 10.1024/2235-0977/a000168.Modeling exploration strategies to predict student performance within a learning environment and beyond
2017-03-13. LAK '17: 7th International Learning Analytics and Knowledge Conference. p. 31-40. DOI : 10.1145/3027385.3027422.Dynamic Bayesian Networks for Student Modeling
IEEE Transactions on Learning Technologies. 2017. DOI : 10.1109/TLT.2017.2689017000418421400005.2016
Evaluation of a Computer-Based Training Program for Enhancing Arithmetic Skills and Spatial Number Representation in Primary School Children
Frontiers in Psychology. 2016-06-27. DOI : 000378607600001.Stealth Assessment in ITS - A Study for Developmental Dyscalculia
2016-06-06. 13th International Conference on Intelligent Tutoring Systems - ITS 2016, Zagreb, Croatia, June 6-10, 2016. p. 79-89. DOI : 10.1007/978-3-319-39583-8_8.When to stop? - Towards universal instructional policies
2016. 6th International Conference on Learning Analytics & Knowledge (LAK '16), Edinburgh, Scotland, UK, April 25-29, 2016. p. 289-298. DOI : 10.1145/2883851.2883961000390844700037.Temporally Coherent Clustering of Student Data
2016. International Conference on Educational Data Mining (EDM), Raleigh, NC, USA, June 29 - July 2, 2016. p. 102-109.2015
Rechenleistung und Fingergnosie: Besteht ein Zusammenhang?: Eine Studie bei Grundschulkindern mit und ohne Rechenschwäche
Lernen und Lernstörungen. 2015-08-01. DOI : 10.1024/2235-0977/a000106.On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models
2015. 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29,2015. p. 37-44.2014
Assistive technology for supporting learning numeracy
Assistive Technology for Cognition; Psychology Press, 2014-12-22. p. 112-128.Different parameters - same prediction. An analysis of learning curves
2014. Educational Data Mining 2014, London, UK, July 4-7, 2014. p. 52-59.Beyond Knowledge Tracing. Modeling Skill Topologies with Bayesian Networks
2014. 12th International Conference, ITS 2014, Honolulu, HI, USA, June 5-9, 2014. p. 188-198. DOI : 10.1007/978-3-319-07221-0_23000343081600023.Computational Education using Latent Structured Prediction
2014. 17th International Conference on Artificial Intelligence and Statistics, AISTATS 2014, Reykjavik, Iceland, April 22-25, 2014. p. 540-548.2013
Modelling and Optimizing Mathematics Learning in Children
International Journal of Artificial Intelligence in Education. 2013-11-01. DOI : 10.1007/s40593-013-0003-7.Design and evaluation of the computer-based training program Calcularis for enhancing numerical cognition
Frontiers in Psychology. 2013-08-05. DOI : 10.3389/fpsyg.2013.00489.Modeling and Optimizing Computer-Assisted Mathematics Learning in Children
ETH-Zürich, 2013. DOI : 10.3929/ethz-a-010265296.Das Mathematikangstinterview (MAI). Erste psychometrische Gütekriterien
Lernen und Lernstörungen. 2013. DOI : 10.1024/2235-0977/a000040.Cluster-based prediction of mathematical learning patterns
2013. 16th international conference on Artificial intelligence in education (AIED 2013), Memphis, TN, USA, July 9-13, 2013. p. 389-399. DOI : 10.1007/978-3-642-39112-5_40.Computerbasierte Lernprogramme für Kinder mit Rechenschwäche
Rechenstörungen bei Kindern. Neurowissenschaft, Psychologie, Pädagogik; Vandenhoeck & Ruprecht, 2013. p. 259-276.2012
Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains
International Journal of Artificial Intelligence in Education. 2012. DOI : 10.3233/JAI-130026.Kinder mit Dyskalkulie fokussieren spontan weniger auf Anzahligkeit
Lernen und Lernstörungen. 2012. DOI : 10.1024/2235-0977/a000024.Modelling and Optimizing the Process of Learning Mathematics
2012. 11th international conference on Intelligent Tutoring Systems (ITS 2012), Chania, Greece, June 14-18, 2012. p. 389-398. DOI : 10.1007/978-3-642-30950-2_50.2011
Therapy software for enhancing numerical cognition
Interdisciplinary perspectives on cognition, education and the brain. 2011. DOI : 10.5167/UZH-64859.Teaching & PhD
Teaching
Communication Systems
Computer Science
PhD Students
Cock Jade Maï L, Güres Fatma-Betül, Mejia Domenzain Paola, Shved Ekaterina, Swamy Vinitra,Courses
Advanced information, computation, communication I
Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics as diverse as mathematical reasoning, combinatorics, discrete structures & algorithmic thinking.