Tanja Käser
tanja.kaeser@epfl.ch +41 21 693 91 12
EPFL IC IINFCOM D-VET
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne
41 21 693 91 12
Office: INF 234
EPFL
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EDIC-ENS
EPFL IC IINFCOM D-VET
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne
Web site: Web site: https://ssc.epfl.ch
EPFL IC IINFCOM D-VET
INF 234 (Bâtiment INF)
Station 14
CH-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.
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
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 : 000331180100001.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