Journal Articles

Rau, M. A. & Beier, J. (in press). Exploring the effects of gesture-based collaboration on students’ benefit from a perceptual training. Journal of Educational Psychology.

Herder, T. & Rau, M. A. (in press). Representational-competency supports in an educational video game for undergraduate astronomy. Computers & Education. doi: https://doi.org/10.1016/j.compedu.2022.104602 (online)

Dorris, M. & Rau, M. A. (in press). Conceptual challenges exhibited by naïve undergraduate students in the context of atomic orbital energy diagrams. Journal of Chemical Education. doi: 10.1021/acs.jchemed.1c01135 (online)

Rau, M. A. & Herder, T. (2021). Under which conditions are physical vs. virtual representations effective? Contrasting conceptual and embodied mechanisms of learning. Journal of Educational Psychology 113(8), 1565–1586. doi: 10.1037/edu0000689 (online)

Rau, M. A., Zahn, M., Misback, E., Herder, T., & Burstyn, J. (2021). Adaptive Support for Representational Competencies during Technology-Based Problem Solving in Chemistry. Journal of the Learning Sciences, 30(2), 163-203. doi: 10.1080/10508406.2021.1888733 (online)

Wu, S. P., Vanveen, B. & Rau, M. A. (2020). How Drawing Prompts Can Increase Cognitive Engagement in an Active Learning Engineering Course. Journal of Engineering Education, 109(4), 723-742. doi: https://doi.org/10.1002/jee.20354 (online)

Donhauser, A., Küchemann, S., Kuhn, J., Rau, M., Malone, S., Edelsbrunner, P., & Lichtenberger, A. (2020). Making the invisible visible: Visualization of the connection between magnetic field, electric current, and Lorentz force with the help of augmented reality. The Physics Teacher, 58(6), 438-439. (online)

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. doi:10.1109/TLT.2019.2902546 (online)

Rau, M. A., (2020). Comparing Multiple Theories about Learning with Physical and Virtual Representations: Conflicting or Complementary Effects? Educational Psychology Review, 32, 297-325. doi: 10.1007/s10648-020-09517-1 (online)

Mason, B., Rau, M. A., & Nowak, R. (2019). Modeling Implicit Knowledge about Visual Representations with Similarity Learning Methods. Cognitive Science, 43(9), e12744. doi: https://doi.org/10.1111/cogs.12744 (online)

Wu, S. P., & Rau, M. A. (2019). How students learn content in Science, Technology, Engineering, and Mathematics (STEM) through drawing activities. Educational Psychology Review, 31(1), 87-120. doi: https://doi.org/10.1007/s10648-019-09467-3 (online)

Wu, S. P., Corr, J. & Rau, M. A. (2019). How instructors frame students’ interactions with educational technologies can enhance or reduce learning with multiple representations. Computers & Education, 128, 199-213. doi: https://doi.org/10.1016/j.compedu.2018.09.012 (online)

Rau, M. A., & Wu, S. P. W. (2018). Combining instructional activities for sense-making processes and perceptual-induction processes involved in connection-making among multiple visual representations. Cognition and Instruction, 36(4), 361-395. doi: https://doi.org/10.1080/07370008.2018.1494179 (online)

Rau, M. A. (2018). Making connections among multiple visual representations: How do sense-making competencies and perceptual fluency relate to learning of chemistry knowledge? Instructional Science, 46(2), 209-243. doi: 10.1007/s11251-017-9431-3 (online)

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. doi: http://dx.doi.org/10.1037/edu0000229 (online)

Wu, S. P. W., & Rau, M. A. (2018). The effectiveness and efficiency of adding drawing prompts to an interactive educational technology when learning with conventional visual representations. Learning and Instruction, 55, 93-104. doi: https://doi.org/10.1016/j.learninstruc.2017.09.010 (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. (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. (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 29(4), 717–761. doi: 10.1007/s10648-016-9365-3 (online)

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. doi: 10.1109/TLT.2016.2623303 (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., 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., 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., & 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., 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. (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

Beier, J. P., & Rau, M. A. (2022). Embodied Learning with Physical and Virtual Manipulatives in an Intelligent Tutor for Chemistry. In V. Dimitrova, N. Matsuda, & M. M. D. T. Rodrigo (Eds.), Artificial Intelligence in Education. AIED 2022. Lecture notes in computer science (pp. 103-114). Springer. (pdf)

Herder, T., & Rau, M. A. (2022). Supporting representational competencies in an educational video game: What does and doesn’t work. In V. Dimitrova, N. Matsuda, & M. M. D. T. Rodrigo (Eds.), Artificial Intelligence in Education. AIED 2022. Lecture notes in computer science (pp. 280-283). Springer. (pdf)

Rau, M. A., & Zahn, M. (2022). Nonverbal collaboration on perceptual learning activities with chemistry visualizations. In V. Dimitrova, N. Matsuda, & M. M. D. T. Rodrigo (Eds.), Artificial Intelligence in Education. AIED 2022. Lecture notes in computer science (pp. 231-235). Springer. (pdf)

Rho, J., & Rau, M. A. (2022). Preparing future learning with novel visuals by support-ing representational competencies. In V. Dimitrova, N. Matsuda, & M. M. D. T. Rodrigo (Eds.), Artificial Intelligence in Education. AIED 2022. Lecture notes in computer science (pp. 66-77). Springer. (pdf)

Rho, J., Rau, M. A., & VanVeen, B. (2022). Investigating growth of representational competencies by knowledge-component model. In A. I. Cristea & C. Brown (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022) (pp. 346-352). International Educational Data Mining Society. (pdf)

Beier, J. P., & Rau, M. A. (2022). The role of visual representations in impasses during collaborative problem solving in undergraduate chemistry. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.). Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 1888-1889). International Society of the Learning Sciences. (pdf)

Ramly, C. M., Sen, A., Kale, V. P., Rau, M. A., & Zhu, X. (2021). Digitally training graph viewers against misleading bar charts. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, CogSci 2021 (pp. 1928-1934). New York, NY: Lawrence Erlbaum. (online)

Rau, M. A., Moore, J. & Burstyn, J. (2020). Do Affordances of Classroom Furniture Affect Learning in Undergraduate Active-Learning Courses? In M. Gresalfi & I. S. Horn (Eds.), The Interdisciplinarity of the Learning Sciences (ICLS) 2020 (Vol. 2, pp. 967-974). Nashville, TN: International Society of the Learning Sciences. (online)

Rau, M. A., Sen, A., & Zhu, X. (2019). Using Machine Learning to Overcome the Expert Blind Spot for Perceptual Fluency Trainings. In M. E. Isotani S., Ogan A., Hastings P., McLaren B., Luckin R. (Ed.), Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science (Vol. 11625, pp. 406-418). Cham: Springer. (pdf)

Rau, M. A., & Schmidt, T. (2019). Disentangling Conceptual and Embodied Mechanisms for Learning with Virtual and Physical Representations. In M. E. Isotani S., Ogan A., Hastings P., McLaren B., Luckin R. (Ed.), Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science (Vol. 11625, pp. 419-431). Cham: Springer. (pdf)

Rau, M. A., Zahn, M., Misback, E., & Burstyn, J. (2019). Adaptive Support for Representation Skills in a Chemistry ITS Is More Effective Than Static Support. In M. E. Isotani S., Ogan A., Hastings P., McLaren B., Luckin R. (Ed.), Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science (Vol. 11625, pp. 432-444). Cham: Springer. (pdf)

Rau, M. A., & Patel, P. (2018). A Collaboration Script for Nonverbal Communication Enhances Perceptual Fluency with Visual Representations. In J. Kay & R. Luckin (Eds.), Rethinking Learning in the Digital Age. Making the Learning Sciences Count (ICLS) 2018 (Vol. 1, pp. 272-279). London, UK: International Society of the Learning Sciences. (pdf)

Rau, M. A., & Zahn, M. (2018). Sequencing Support for Sense Making and Perceptual Fluency with Visual Representations: Is There a Learning Progression? In J. Kay & R. Luckin (Eds.), Rethinking Learning in the Digital Age. Making the Learning Sciences Count (ICLS) 2018 (Vol. 1, pp. 264-271). London, UK: International Society of the Learning Sciences. (pdf)

Sen, A., Purav, P., Rau, M.A., Mason, B., Nowak, R., Rogers, T., & Zhu, X. (2018). Machine Beats Human at Finding the Optimal Sequence of Visual Representations for Students Learning of Perceptual Fluency. In K. E. Boyer & M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 137-146). Buffalo, NY: International Educational Data Mining Society. (pdf)

Wu, S. P. & Rau, M. A. (2018). Collaboration Scripts Should Focus on Shared Models, Not on Drawings, to Help Students Translate Between Representations. In J. Kay & R. Luckin (Eds.), Rethinking Learning in the Digital Age. Making the Learning Sciences Count (ICLS) 2018 (Vol. 1, pp. 264-271). London, UK: International Society of the Learning Sciences. (pdf)

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. (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., 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., & 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)

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)

Book Chapters

Koedinger, K. R., Rau, M. A., & McLaughlin, E. A. (in press). Different goals imply different methods: A guide to adapting instructional methods to your context. In C. E. Overson, C. M. Hakala, L. L. Kordonowy, & V. A. Benassi (Eds.), In their own words: What scholars want you to know about why and how to apply the science of learning in your academic setting. Society for the Teaching of Psychology.

Rau, M. A. (in press). Adaptive Support for Representational Competencies during Technology-Based Problem Solving in STEM. Jiao, P., McLaren, B., Alavi, A. H., Ouyang, F. (Eds.), Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology. Boca Raton, FL: CRC Press Taylor & Francis Group.

Rau, M. A. (2020). Cognitive and socio-cultural theories on competencies and practices involved in learning with multiple representations. Van Meter, P., List, A., Lombardi, D., & Kendeou (Eds.), Handbook of Learning from Multiple Representations and Perspectives. New York, NY: Routledge.

Rau, M. A. & Moore, J. (2020). Flipped classrooms and collaborative support in chemistry. Mintzes, J. J. & Walter, E. M. (Eds.), Active Learning in College Science: The Case for Evidence-Based Practice (pp. 567-582). Dordrecht, Netherlands: Springer.

Rau, M. A. (2018). Supporting representational competences through adaptive educational technologies. In K. Halverson Daniel (Ed.), Towards a Framework for Representational Competence in Science Education (pp. 103-132). Dordrecht, Netherlands: Springer.

Rau, M. A. (2016). 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.