Associate Professor, Learning Sciences Area
1086 Educational Sciences
1025 West Johnson Street
Madison, WI 53706
Download CV   Rau Lab Website
Martina Rau is an Associate Professor in the Department of Educational Psychology in the area of Learning Sciences and is affiliated with the Department of Computer Sciences. She received her Ph.D. in Human-Computer Interaction from Carnegie Mellon University. Her research focuses on learning with multiple external representations in educational technologies. She uses a multi-methods approach to integrate learning outcome measures and process-level measures.
- PhD Human-Computer Interaction, Carnegie Mellon University, 2013
- MS Human-Computer Interaction, Carnegie Mellon University, 2010
Select Media Contributions
- Martina Rau, YouTube, Misinterpretations of chemistry visualizations. (August, 2022). Media Link
- Martina Rau, YouTube, Involving students from multiple disciplines in my research. (December, 2017). Media Link
- Rau, M. A., Zahn, M., Misback, E., Herder, T., & Burstyn, J. Adaptive Support for Representational Competencies during Technology-Based Problem Solving in Chemistry. Journal of the Learning Sciences, 30(2), 163-203.
- Rau, M. A., (2020). Comparing Multiple Theories about Learning with Physical and Virtual Representations: Conflicting or Complementary Effects?. Educational Psychology Review, 32, 297-325.
- Rau, M. A., Keesler, W., Zhang, Y., & Wu, S. (2020). Resolving Design Tradeoffs of Interactive Visualization Tools for Educational Technologies. IEEE Transactions on Learning Technologies, 13(2), 326-339.
- Mason, B., Rau, M. A., & Nowak, R. (2019). Modeling Implicit Knowledge about Visual Representations with Similarity Learning Methods. Cognitive Science, 43(9), e12744. Online Publication/Abstract.
- Rau, M. A., (2018). Sequencing Sense-Making Support and Fluency-Building Support for Connection Making among Multiple Visual Representations. Journal of Educational Psychology, 110(6), 811-833. Online Publication/Abstract.
- Rau, M. A., (2017). A framework for discipline-specific grounding of educational technologies with multiple visual representations. IEEE Transactions on Learning Technologies, 10(3), 290-305.
- Rau, M. A., Bowman, H. E., & Moore, J. W. (2017). An adaptive collaboration script for learning with multiple visual representations. Computers & Education, 109(C), 38-55. DOI Link.
- Rau, M. A., (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning.. Educational Psychology Review, 29(4), 717-761.
- Rau, M. A., Aleven, V., & Rummel, N. (2015). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1) (pp. 30-46).
- Rau, M. A., Aleven, V., & Rummel, N. (2013). Interleaved practice in multi-dimensional learning tasks: which dimension should we interleave?. Learning and Instruction, 23, 98-114.
- Rau, M. A., Rummel, N., & Aleven, V. Selbsterklärungs-Prompts helfen Schülern beim Lernen mit multiplen graphischen Repräsentationen von Brüchen. presented at the 12. Fachtagung Pädagogische Psychologie der DGPs, Saarbrücken, Germany.
- Rau, M. A., Aleven, V., & Rummel, N. Self-explanations prompts enable students to benefit from learning with multiple graphical re-presentations of fractions. presented at the 13th Biennial EARLI Conference for Research on Learning and Instruction, Amsterdam, Netherlands.
- Rau, M. A., & Schmidt, T. (2019). Disentangling Conceptual and Embodied Mechanisms for Learning with Virtual and Physical Representations. Paper presented at the Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science.
- Rau, M. A., Sen, A., & Zhu, X. (2019). Using Machine Learning to Overcome the Expert Blind Spot for Perceptual Fluency Trainings. Paper presented at the Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science.
- Rau, M. A., & Patel, P. (2018). A Collaboration Script for Nonverbal Communication Enhances Perceptual Fluency with Visual Representations. Paper presented at the Rethinking Learning in the Digital Age. Making the Learning Sciences Count (ICLS) 2018, London, UK.
- Rau, M. A., & Wu, S. P. W. (2017). Educational Technology Support for Collaborative Learning With Multiple Visual Representations in Chemistry. Paper presented at the Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017, Philadelphia, PA.
- Rau, M. A., Mason, B., & Nowak, R. (2016). How to model implicit knowledge? Use of metric learning to assess student perceptions of visual representations. Paper presented at the Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, NC.
- Rau, M. A. (2016). Pattern mining uncovers social prompts of conceptual learning with physical and virtual representation. Paper presented at the Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, NC.
- Rau, M. A. (2015). Why Do the Rich Get Richer? A Structural Equation Model to Test How Spatial Skills Affect Learning with Representations. Paper presented at the Proceedings of the 8th International Conference on Educational Data Mining.
- Rau, M. A., Scheines, R., Aleven, V., & Rummel, N. (2013). Does Representational Understanding Enhance Fluency – Or Vice Versa? Searching for Mediation Models. Paper presented at the Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013).
Select Awards and Honors
- Early Career Award, International Society of the Learning Sciences, 2021
- Postdoctoral Fellowship, NAEd/Spencer, 2019
- Postdoctoral Fellowship Semifinalist, NAEd/Spencer, 2018
- VIP (Visiting International Professor), Ruhr-Universität, 2018
- CAREER Award, National Science Foundation, 2017
- Best Paper Nomination, 9th International Conference on Educational Data Mining (EDM 2016), 2016
- Best Paper Award, 6th International Conference on Educational Data Mining (EDM 2013), 2013
- Best Paper Nomination, 2013 SIGCHI Conference on Human Factors in Computing Systems (CHI 2013), 2013
- Siebel Scholar, Class of 2013, Siebel Foundation, 2013
- Best Student Paper Award, 14th International Conference on Artificial Intelligence in Education, 2009