rau-lab

Publications

Spotlight Papers

Our research on active learning in chemistry just came out in the Journal of Chemical Education: Rau et al. (2017)

Chem Tutor helps students collaboratively connect 3D and 2D models: Rau et al. (2017)

We recently published our theoretical framework in the Educational Psychology Review: Rau (2016)

Journal Articles

Rau, M. A. (2017). Sequencing Sense-Making Support and Fluency-Building Support for Connection Making among Multiple Visual Representations. Journal of Educational Psychology.

Rau, M. A., Moore, J., Kennedy, K., Oxtoby, L., & Bollom, M. (2017). Unpacking “active learning” interventions: A combination of flipped classroom and collaboration support is more effective but collaboration support alone is not. Journal of Chemical Education. doi: 10.1021/acs.jchemed.7b00240 (online)

Rau, M. A., & Matthews, P. (2017). How to make ‘more’ better? Principles for effective use of multiple representations to enhance student learning. ZDM - Mathematics Education, 49(4), 491-496. doi: 10.1007/s11858-017-0846-8 (online)

Rau, M. A., Bowman, H., & Moore, J. (2017). Intelligent technology-support for collaborative connection-making among multiple visual representations in chemistry. Computers and Education, 109, 38-55. doi: 10.1016/j.compedu.2017.02.006 (online)

Rau, M. A. (2017). Exploring how educational technology support affects the role of teachers as students collaborate make sense of visual representations: A multi-methods study on high-school chemistry learning. Journal of Learning Analytics, 4(2), 240-263. doi: http://dx.doi.org/10.18608/jla.2017.42.16 (online)

Rau, M. A., Aleven, V., & Rummel, N. (2017). Making connections among multiple graphical representations of fractions: sense-making competencies enhance perceptual fluency, but not vice versa. Instructional Science, 45(3), 331-357. doi: 10.1007/s40593-016-0134-8 (online)

Rau, M. A. (2017). Do knowledge-component models need to incorporate representational competencies? International Journal of Artificial Intelligence in Education, 27(2), 298-319. doi: http://dx.doi.org/10.1037/edu0000145 (online)

Rau, M. A., Aleven, V., & Rummel, N. (2017). Supporting students in making sense of connections and in becoming perceptually fluent in making connections among multiple graphical representations. Journal of Educational Psychology, 109(3), 355-373. doi: http://dx.doi.org/10.1037/edu0000145 (online)

Rau, M. A. (2016). A framework for discipline-specific grounding of educational technologies with multiple visual representations. IEEE Transactions on Learning Technologies. doi: 10.1109/TLT.2016.2623303 (online)

Rau, M. A. (2016). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 1-45. doi: 10.1007/s10648-016-9365-3 (online)

Rau, M. A. (2015). Enhancing undergraduate chemistry learning by helping students make connections among multiple graphical representations. Chemistry Education Research and Practice, 16, 654-669. doi: 10.1039/C5RP00065C (online)

Rau, M. A., Michaelis, J. E., & Fay, N. (2015). Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry. Computers and Education, 82, 460-485. doi:10.1016/j.compedu.2014.12.009 (online)

Rau, M. A., Aleven, V., & Rummel, N. (2015). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1), 30-46. doi:10.1037/a0037211 (online)

Rau, M. A., Aleven, V., Rummel, N., & Pardos, Z. (2014). How should Intelligent Tutoring Systems sequence multiple graphical representations of fractions? A multi-methods study. International Journal of Artificial Intelligence in Education, 24(2), 125-161. doi: 10.1007/s40593-013-0011-7 (online)

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. doi: 10.1016/j.learninstruc.2012.07.003 (online)


Peer Reviewed Conference Papers

Sharma, K., Jermann, P., Dillenbourg, P., Rau, M., Pardos, Z., Schneider, B., D’Angelo, S., Gergle, D., & Prieto, L. (2017). CSCL and Eye-tracking: Experiences, Opportunities and Challenges. In B. K. Smith, M. Borge, E. Mercier & K. Y. Lim (Eds.), Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017 (Vol. 2, pp. 727-734). Philadelphia, PA: International Society of the Learning Sciences. (online)

Rau, M. A., & Wu, S. P. W. (2017). Educational Technology Support for Collaborative Learning With Multiple Visual Representations in Chemistry. In B. K. Smith, M. Borge, E. Mercier & K. Y. Lim (Eds.), Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017 (Vol. 1, pp. 79-86). Philadelphia, PA: International Society of the Learning Sciences. (online)

Wu, S. P. W., & Rau, M. A. (2017). How Technology and Collaboration Promote Formative Feedback: A Role for CSCL Research in Active Learning Interventions. In B. K. Smith, M. Borge, E. Mercier & K. Y. Lim (Eds.), Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017 (Vol. 1, pp. 279-286). Philadelphia, PA: International Society of the Learning Sciences (online)

Rau, M. A., Mason, B., & Nowak, R. (2016). How to model implicit knowledge? Use of metric learning to assess student perceptions of visual representations. In T. Barnes, M. Chi & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 199-206). Raleigh, NC: International Educational Data Mining Society. Honorable Mention. (pdf)

Rau, M. A. (2016). Association rules uncover social triggers of conceptual learning with physical and virtual representation In S. Barnes, M. Chi & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 478-483). Raleigh, NC: International Educational Data Mining Society. (pdf)

Rau, M. A. (2015). Why do the rich get richer? A structural equation model to test how spatial skills affect learning with representations. In J. G. Boticario, O. C. Santos, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura & M. Desmarais (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (pp. 350-357). (pdf)

Rau, M. A., & Wu, S. P. W. (2015). ITS support for conceptual and perceptual processes in learning with multiple graphical representations. In C. Conati, N. Heffernan, A. Mitrovic & M. F. Verdejo (Eds.), Artificial Intelligence in Education (pp. 398–407). Switzerland: Springer International Publishing. (pdf)

Peterson, J., Pardos, Z., Rau, M., Swigart, A., Gerber, C., & McKinsey, J. (2015). Understanding student success in chemistry using gaze tracking & pupillometry. In C. Conati, N. Heffernan, A. Mitrovic & M. F. Verdejo (Eds.), Artificial Intelligence in Education (pp. 358–366). Switzerland: Springer International Publishing. (pdf)

Rau, M. A., & Evenstone, A. L. (2014). Multi-methods approach for domain-specific grounding: An ITS for connection making in chemistry. In S. Trausan-Matu et al. (Ed.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (pp. 426-435). (pdf)

Rau, M. A., Aleven, V., & Rummel, N. (2014). Sequencing sense-making and fluency-building support for connection making between multiple graphical representations. In J. L. Polman, E. A. Kyza, D. K. O’Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O’Connor, T. Lee & L. D’Amico (Eds.), Learning and Becoming in Practice: The International Conference of the Learning Sciences 2014 (Vol. 2, pp. 977-981). Boulder, CO: International Society of the Learning Sciences. (pdf)

Rau, M., Aleven, V., Rummel, N., & Rohrbach, S. (2013). Why interactive learning environments can have it all: Resolving design conflicts between conflicting goals. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 109-118). Honorable Mention. (pdf)

Rau, M., Aleven, V., & Rummel, N. (2013). How to use multiple graphical reprsentations to support conceptual learning? Research-based principles in the Fractions Tutor. In H. C. Lane, K. Yacef, J. Mostow & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 762-765). Berlin Heidelberg: Springer. (pdf)

Rau, M., Aleven, V., & Rummel, N. (2013). Complementary effects of sense-making and fluency-building support for connection making: A matter of sequence? In H. C. Lane, K. Yacef, J. Mostow & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 329-338). Berlin Heidelberg: Springer. (pdf)

Rau, M. A., Scheines, R., Aleven, V., & Rummel, N. (2013). Does representational understanding enhance fluency or vice versa? Searching for mediation models. In S. K. D’Mello, R. A. Calvo & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013) (pp. 161-169). Best paper award. (pdf)

Carlson, R., Genin, K., Rau, M., & Scheines, R. (2013). Student profiling from tutoring system log data: When do multiple graphical representations matter? In S. K. D’Mello, R. A. Calvo & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013) (pp. 12-20). (pdf)

Rau, M., Aleven, V., Rummel, N., & Rohrbach, S. (2012). Sense making alone doesn’t do it: Fluency matters too! ITS support for robust learning with multiple representations. In S. Cerri, W. Clancey, G. Papadourakis & K. Panourgia (Eds.), Intelligent Tutoring Systems (Vol. 7315, pp. 174-184). Berlin / Heidelberg: Springer. (pdf)

Rau, M. A., & Pardos, Z. A. (2012). Investigating practice schedules of multiple fraction representations using knowledge tracing based learning analysis techniques. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining (pp. 168-171). (pdf)

Rau, M. A., & Scheines, R. (2012). Searching for variables and models to investigate mediators of learning from multiple representations. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining (pp. 110-117). (pdf)

Matlen, B., Atit, K., Goksun, T., Rau, M., & Ptouchkina, M. (2012). Representing space: Exploring the relationship between gesturing and geoscience understanding in children. In C. Stachniss, K. Schill & D. Uttal (Eds.), Spatial Cognition VIII (Vol. 7463, pp. 405-415). Berlin Heidelberg: Springer Berlin Heidelberg. (pdf)

Hayashi, E., Rau, M. A., Neo, Z. H., Tan, N., Ramasubramanian, S., & Paulos, E. (2012). TimeBlocks: Mom, can I have another block of time? In Proceedings of the 2012 ACM Conference on Human Factors in Computing Systems (CHI 2012) (pp. 1713-1716). New York, NY: ACM. (pdf)

Rau, M., Rummel, N., Aleven, V., Pacilio, L., & Tunc-Pekkan, Z. (2012). How to schedule multiple graphical representations? A classroom experiment with an intelligent tutoring system for fractions. In J. van Aalst, K. Thompson, M. J. Jacobson & P. Reimann (Eds.), The future of learning: Proceedings of the 10th International Conference of the Learning Sciences 2012 (Vol. 1, pp. 64-71). Sydney, Australia: ISLS. (pdf)

Feenstra, L., Aleven, V., Rummel, N., Rau, M. A., & Taatgen, N. (2011). Thinking with your hands: Interactive graphical representations in a tutor for fractions learning. Lecture Notes in Computer Science, 6738, 453-455.

Rau, M. A., Aleven, V., & Rummel, N. (2010). Blocked versus interleaved practice with multiple representations in an intelligent tutoring system for fractions. In V. Aleven, J. Kay, & J. Mostow (Eds.), Proceedings of the 10th International Conference of Intelligent Tutoring Systems (pp. 413-422). Heidelberg / Berlin: Springer. (pdf)

Rau, M. A., Aleven, V., & Rummel, N. (2009). Intelligent tutoring systems with multiple representations and self-explanation prompts support learning of fractions. In V. Dimitrova, R. Mizoguchi, & B. du Boulay (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 441-448). Amsterdam, the Netherlands: IOS Press. Best student paper award. (pdf)


Peer Reviewed Short Papers

Rau, M. A. (2016). Social, perceptual, and conceptual factors of learning with multiple external representations in educational technologies. In C.-K. Looi, J. Polman, U. Cress & P. Reimann (Eds.), Proceedings of the International Conference of the Learning Sciences 2016 (Vol. 2, pp. 1378-1379). Singapore: ISLS. (pdf)

Rau, M. A., & Pardos, Z. A. (2016). Adding eye-tracking AOI data to models of representation skills does not improve prediction accuracy. In S. Barnes, M. Chi & M. Feng (Eds.), Proceedings of the International Conference on Educational Data Mining (pp. 622-623). Raleigh, NC: International Educational Data Mining Society. (pdf)

Rau, M. A., Wu, S. P., & Schuberth, J. (2016). Sequencing physical representations with human tutors and virtual representations with a computer tutor in chemistry. In C.-K. Looi, J. Polman, U. Cress & P. Reimann (Eds.), Proceedings of the International Conference of the Learning Sciences 2016 (Vol. 2, pp. 1173-1174). Singapore: ISLS. (pdf)


Book Chapters

Rau, M. A. (in press). Supporting students’ learning with multiple visual representations. In J. C. Horvath, J. Lodge & J. A. C. Hattie (Eds.), From the Laboratory to the Classroom: Translating the Learning Sciences for Teachers (pp. 155-171). New York, NY: Routledge Press.

Rau, M. A. (2016). Supporting representational competences through adaptive educational technologies. In K. Halverson Daniel & J. Gilbert (Eds.), Towards a Framework for Representational Competence in Science Education. Dordrecht, Netherlands: Springer.