STEM

Science Reasoning & Engineering Design

    Featured
  1. Paletz, S., Chan, J., & Schunn, C. D. (2017). Dynamics of micro-conflicts and uncertainty in successful and unsuccessful design teams. Design Studies, 50, 39-69. 10.1016/j.destud.2017.02.002 pdf
  2. Chan, J., Dow, S. P., & Schunn, C. D. (2015). Do the best design ideas (really) come from conceptually distant sources of inspiration? Design Studies, 36, 31-58. pdf
  3. Paletz, S. B. F., Kim, K., Schunn, C. D., Tollinger, I., & Vera, A. (2013). The development of adaptive expertise, routine expertise, and novelty in a large research team. Applied Cognitive Psychology, 27(4), 415–428. pdf
  4. Christensen, B. T., & Schunn, C. D. (2007). The relationship of analogical distance to analogical function and pre-inventive structure: The case of engineering design. Memory & Cognition, 35(1), 29-38. pdf
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  5. Chan, J., & Schunn, C. D. (2023). The importance of separating appropriateness into impact and feasibility for the psychology of creativity. Creativity Research Journal, 35(4), 629–644. 10.1080/10400419.2023.2191919 pdf
  6. Goncher, A., Chan, J., & Schunn, C. D. (2017). Measuring design innovation for project-based design assessment: considerations of robustness and efficiency. Bitacora Urbano Territorial, 27(4), 19-30. pdf
  7. Egan, P., Moore, J., Ehrlicher, A., Weitz, D., Schunn, C., Cagan, J., & LeDuc, P. (2017). Robust mechanobiological behavior emerges in heterogenous myosin systems. Proceedings of the National Academy of Sciences, 114(39), E8147–E8154. 10.1073/pnas.1713219114 pdf
  8. Paletz, S., Chan, J., & Schunn, C. D. (2017). Dynamics of micro-conflicts and uncertainty in successful and unsuccessful design teams. Design Studies, 50, 39-69. 10.1016/j.destud.2017.02.002 pdf
  9. Egan, P., Chiu, F., Cagan, J., Schunn, C., LeDuc, P., Moore, J. (2016). The d3 Design Methodology: Computational and cognitive-based processes for discovering describing and developing bio-based technologies. Journal of Mechanical Design, 138, 081101-1-13. pdf
  10. Paletz, S., Chan, J., & Schunn, C. D. (2016). Uncovering uncertainty through disagreement. Applied Cognitive Psychology, 30(3), 387–400. pdf
  11. Egan, P., Schunn, C., Cagan, J., & LeDuc, P. (2015). Improving understanding and design proficiency of complex multi-level biosystems through animation and parametric relationships support. Design Science, 1(1), e3. doi:10.1017/dsj.2015.3 pdf
  12. Chan, J., & Schunn, C. D. (2015). The importance of iteration in creative conceptual combination. Cognition, 145, 104–115. doi:10.1016/j.cognition.2015.08.008. pdf
  13. Egan, P., Schunn, C. D., Cagan, J., & LeDuc, P. (2015). Emergent systems energy laws for predicting myosin ensemble processivity. PLOS Computational Biology, 11(4): e1004177. link
  14. Egan, P., Cagan, J., Schunn, C. D., & LeDuc, P. (2015). Synergistic human-agent methods for deriving effective search strategies: The case of nanoscale design. Research In Engineering Design, 26(2), 145-169. pdf
  15. Chan, J., Dow, S. P., & Schunn, C. D. (2015). Do the best design ideas (really) come from conceptually distant sources of inspiration? Design Studies, 36, 31-58. pdf
  16. Chan, J., & Schunn, C. D. (2015). The impact of analogies on creative concept generation: Lessons from an in vivo study in engineering design. Cognitive Science, 39(1), 126-155. pdf
  17. Paletz, S. B. F., Kim, K., Schunn, C. D., Tollinger, I., & Vera, A. (2013). The development of adaptive expertise, routine expertise, and novelty in a large research team. Applied Cognitive Psychology, 27(4), 415–428. pdf
  18. Egan, P. F., Cagan, J. C., Schunn, C. D., & LeDuc, P. R. (2013). Design of complex biologically-based nanoscale systems using multi-agent simulations and structure-behavior-function representations. Journal of Mechanical Design, 135(6), 061005. pdf
  19. Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C., & Wood, K. (2013). The meaning of “near” and “far”: The impact of structuring design databases and the effect of distance of analogy on design output. Journal of Mechanical Design, 135(2), 021007. pdf
  20. Fu, K., Chan, J., Schunn, C. D., Cagan, J., & Kotovsky, K. (2013). Expert representation of design repository space: A comparison to and validation of algorithmic output. Design Studies, 34(6), 729-762. pdf
  21. Paletz, S. B. F., Schunn, C. D., & Kim, K. (2013). The interplay of conflict and analogy in multidisciplinary teams. Cognition, 126(1), 1-19. pdf
  22. Chan, J., Paletz, S., & Schunn, C. D. (2012). Analogy as a strategy for supporting complex problem solving under uncertainty. Memory & Cognition, 40, 1352-1365. pdf
  23. Paletz, S. B. F., & Schunn, C. D. (2012). Digging into implicit/explicit states and processes: The case of cognitive/social process interaction in scientific groups. In R. Proctor and J. Capaldi (Eds.), Psychology of Science: Implicit and Explicit Processes. Oxford University Press. pdf
  24. Schunn, C. D., & Trafton, J. G. (2012). The psychology of uncertainty in scientific data analysis. In G. Feist & M. Gorman (Eds.), Handbook of the Psychology of Science. Springer Publishing. pdf
  25. Chan, J., Fu, K., Schunn, C. D., Cagan, J., Wood, K., & Kotovsky, K. (2011). On the benefits and pitfalls of analogies for innovative design: Ideation performance based on analogical distance, commonness, and modality of examples. Journal of Mechanical Design, 133, 081004-1-11. pdf
  26. Paletz, S. B. F., Schunn, C. D., & Kim, K. (2011). Intragroup conflict under the microscope: micro-conflicts in naturalistic team discussions. Negotiation and Conflict Management Research, 4, 314-351. pdf
  27. Paletz, S. B. F., & Schunn,C. D. (2011). Assessing group level participation in fluid teams: Testing a new metric. Behavior Research Methods, 10.3758/s13428-011-0070-3. pdf
  28. Schunn, C. D. (2010). From uncertainly exact to certainly vague: Epistemic uncertainty and approximation in science and engineering problem solving. In B. Ross (Ed.), Psychology of Learning and Motivation (Vol. 53). pdf
  29. Linsey, J., Tseng, I., Fu, K., Cagan, J., Wood, K., & Schunn, C. D. (2010). A study of design fixation, its mitigation and perception in engineering design faculty. Journal of Mechanical Design, 132(4), 041003-1-12. pdf
  30. Paletz, S. B. F., & Schunn, C. D. (2010). A social-cognitive framework of multidisciplinary team innovation. Topics in Cognitive Science, 2, 73-95. pdf
  31. Christensen, B. T., & Schunn, C. D. (2009). Setting a limit to randomness [or: ‘Putting blinkers on a blind man’]: Providing cognitive support for creative processes with environmental cues. In K. Wood & A. Markman (Eds.), Tools for Innovation. Oxford University Press. pdf
  32. Trickett, S. B, Trafton, J. G., & Schunn, C. D. (2009). How do scientists respond to anomalies? Different strategies used in basic and applied science. Topics in Cognitive Science, 1(4), 711-729. pdf
  33. Christensen,B. T., & Schunn, C. D. (2009). The role and impact of mental simulation in design. Applied Cognitive Psychology, 23, 327-344. pdf
  34. Christensen, B. T., & Schunn,C. D. (2007). The relationship of analogical distance to analogical function and pre-inventive structure: The case of engineering design. Memory & Cognition, 35(1), 29-38. pdf
  35. Schunn, C. D., Saner, L. D., Kirschenbaum, S. K., Trafton, J. G., & Littleton, E. B. (2007). Complex visual data analysis, uncertainty, and representation. In M. C. Lovett & P. Shah (Eds.), Thinking with Data. Mahwah, NJ: Erlbaum. pdf
  36. Trickett, S. B., Trafton, J. G., & Saner, L. D., & Schunn, C. D. (2007). "I don't know what is going on there": The use of spatial transformations to deal with and resolve uncertainty in complex visualizations. In M. C. Lovett & P. Shah (Eds.), Thinking with Data. Mahwah, NJ: Erlbaum. pdf
  37. Mehalik, M. M., & Schunn, C. D. (2006). What constitutes good design? A review of empirical studies of the design process. International Journal of Engineering Education, 22(3), 519-532. pdf
  38. Trafton,J. G., Trickett, S. B., Stitzlein, C. A., Saner, L. D., Schunn, C. D., & Kirschenbaum, S. S. (2006). The relationship between spatial transformations and iconic gestures. Spatial Cognition & Computation, 6(1), 1-29. pdf
  39. Schunn, C. D., Crowley, K., & Okada, T. (2005). Cognitive Science: Interdisciplinarity now and then. In S. J. Derry, C. D. Schunn, & M. A. Gernsbacher (Eds.), Problems and promises of interdisciplinary collaboration: Perspectives from cognitive science. Mahwah, NJ: Erlbaum. pdf
  40. Trickett, S. B., Trafton, J. G., & Schunn, C. D. (2005). Puzzles and peculiarities: How scientists attend to and process anomalies during data analysis. In M. E. Gorman, R. D. Tweney, D. Gooding, & A. Kincannon (Eds.), Scientific and technological thinking (pp. 97-118). Mahwah, NJ: LEA.pdf
  41. Christensen, B. T., & Schunn, C. D. (2005). Spontaneous access and analogical incubation effects. Creativity Research Journal, 17(2), 207-220. pdf
  42. Schunn, C. D., Crowley, K., & Okada, T. (2002). What makes collaborations across a distance succeed?: The case of the Cognitive Science community. In P. Hinds & S. Kiesler (Eds.), Distributed Work. Cambridge, MA: MIT Press. pdf
  43. Schunn, C. D., Crowley, K., & Okada, T. (2000). Cognitive Science: Interdisciplinarity now and then. In K. Ueda & T. Okada (Eds.), (2000). In search of collaborative cognition: Cognitive science on creative collaboration. Tokyo: Kyoritsu Shuppan. (In Japanese)
  44. Schunn, C. D., & Klahr, D. (2000). Discovery processes in a more complex task. In D. Klahr (Ed.), Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press. Download programs. pdf
  45. Schunn, C. D., & Anderson, J. R. (1999). The generality/specificity of expertise in scientific reasoning. Cognitive Science, 23(3), 337-370. pdf
  46. Schunn, C. D., & Anderson, J. R. (1998). Scientific Discovery. In J. R. Anderson & C. Lebiere (Eds.), Atomic Components of Thought. Mahwah, NJ: Erlbaum. pdf
  47. Schunn, C. D., Crowley, K., & Okada, T. (1998). The growth of multidisciplinarity in the Cognitive Science Society. Cognitive Science, 22(1), 107-130. pdf
  48. Schunn, C. D., & Dunbar, K. (1996). Priming, analogy, and awareness in complex reasoning. Memory & Cognition, 24(3), 271-284. pdf

Science, Technology, Engineering, and Mathematics Learning

    Featured
  1. Cannady, M. A., Vincent-Ruz, P., Chung, J. M., & Schunn, C. D. (2019). Scientific sensemaking supports science content learning across disciplines and instructional contexts. Contemporary Educational Psychology, 59, 101802. pdf
  2. Pareja Roblin, N., Schunn, C., & McKenney, S. (2018). What Are Critical Features of Science Curriculum Materials that Impact Student and Teacher Outcomes? Science Education, 102(2), 260-282. pdf
  3. Schuchardt, A., & Schunn, C. D. (2016). Modeling scientific processes with mathematics equations enhances student qualitative conceptual understanding and quantitative problem solving. Science Education, 100(2), 290–320. pdf
  4. Tekkumru-Kisa, M., Stein, M. K., & Schunn, C. D. (2015). A framework for analyzing cognitive demand and content-practices integration: Task analysis guide in science. Journal of Research in Science Teaching, 52(5), 659-685. pdf
  5. Mehalik, M. M., & Doppelt, Y., & Schunn, C. D. (2008). Middle-school science through design-based learning versus scripted inquiry: Better overall science concept learning and equity gap reduction. Journal of Engineering Education, 97(1), 71-85. pdf
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  6. Avery, M., Caldwell, J., Schunn, C. D., & Wolfe, K. (in press). Improving introductory economics course content and delivery improves outcomes for women. Journal of Economic Education. 10.1080/00220485.2024.2334041 pdf
  7. Huang, Y., Brusilovsky, P., Guerra, J., Koedinger, K., & Schunn, C. D. (2023). Supporting skill integration in an intelligent tutoring system for code tracing. Journal of Computer Assisted Learning, 39(2), 477-500. 10.1111/jcal.12757 pdf
  8. Walsh, M. E., Witherspoon, E. B, Schunn, C. D. & Matsumura, L. C. (2023). Mental simulations to facilitate teacher learning of ambitious mathematics instruction in coaching interactions. International Journal of STEM Education, 10(9). 10.1186/s40594-023-00401-2pdf
  9. Fischer, C., Witherspoon, E., Nguyen, H., Feng, Y., Fiorini, S., Vincent-Ruz, P., Mead, C., Bork, W. Matz, R., & Schunn, C. D. (2023). Advanced Placement course credit and undergraduate student success in gateway science courses. Journal of Research in Science Teaching, 60(2), 304-329. 10.1002/tea.21799 pdf
  10. Kiselyov, K., & Schunn, C. D. (2022). Storytelling as a tool to enhance conceptual understanding in cell biology. Journal of Microbiology and Biology Education, 23(2), e00308-21. pdf
  11. Miller-Cotto, D., & Schunn, C. D. (2022). Mind the gap: How a large-scale course re-design in economics reduced performance gaps. The Journal of Experimental Education, 90(4), 783-796.pdf
  12. Witherspoon, E. B, Ferrer, N. B., Correnti, R., Stein, M. K., & Schunn, C. D. (2021). Coaching that supports teachers’ learning to enact ambitious instruction. Instructional Science, 49, 877–898.pdf
  13. Liu, A. S., & Schunn, C. D. (2020). Predicting pathways to optional summer science experiences by socioeconomic status and the impact on science attitudes and skills. International Journal of STEM Education, (7), 49. link
  14. Bernstein, D., Drayton, B., McKenney, S. E., & Schunn, C. D. (2020). Consequences of curricular adaptation strategies for implementation at scale. Science Education, 104(6), 983-1007. pdf
  15. Vincent-Ruz, P., Meyer, T., Garrett-Roe, S. & Schunn, C. D. (2020). Short and long term effects of POGIL in a large enrollment General Chemistry course. Journal of Chemical Education, 97(5), 1228-1238. pdf
  16. Whitcomb, K. M., Kalender, Z. Y., Nokes-Malach, T. J., Schunn, C. D., & Singh, C. (2020). Laying a foundation for success in engineering coursework: A predictive curriculum model. International Journal of Engineering Education, 36(4), 1340–1355. pdf
  17. Hosseini, R., Akhuseyinoglu, K., Brusilovsky, P., Malmi, L., Pollari-Malmi K., Schunn, C. D., & Sirkiä, T. (2020). Improving engagement in program construction examples for learning python programming. International Journal of Artificial Intelligence in Education, 30 (2), 299-236. html
  18. Tekkumru-Kisa, M., & Schunn, C. D. (2019). Integrating a space for teacher interaction into an educative curriculum: Design principles and teachers’ use of the iPlan tool. Technology, Pedagogy and Education, 28(2), 133-155. pdf
  19. Cannady, M. A., Vincent-Ruz, P., Chung, J. M., & Schunn, C. D. (2019). Scientific sensemaking supports science content learning across disciplines and instructional contexts. Contemporary Educational Psychology, 59, 101802. pdf
  20. Huang, X., Wang, Y., Schunn, C. D., Zou, Y., & Ai, W. (2019). Redesigning flipped classrooms: A learning model and its effects on student perceptions. Higher Education, 78, 711-728. html
  21. Quintana, R., & Schunn, C. D. (2019). Who benefits from a foundational logic course? Effects on undergraduate course performance. Journal of Research on Educational Effectiveness, 12(2), 191-214. pdf
  22. Witherspoon, & Schunn, C. D. (2019). Teachers’ goals predict computational thinking gains in robotics. Information and Learning Science, 120(5/6), 308-326. pdf
  23. Betancur, L., Rottman, B. M., Votruba-Drzal, E., & Schunn, C. D. (2019). Analytical assessment of course sequencing: The case of methodological courses in psychology. Journal of Educational Psychology, 111(1), 91-103. pdf
  24. Tekkumru-Kisa, M., Schunn, C. D., Stein, M. K., & Reynolds, B. (2019). Change in thinking demands for students across the phases of a science task: An exploratory study. Research in Science Education, 49(3), 859-883. pdf
  25. McKenney, S. E., & Schunn, C. D. (2018). How can educational research support practice at scale? Attending to educational designer needs. British Educational Research Journal , 44(6), 1084-1100. pdf
  26. Betancur, L., Votruba-Drzal, E., & Schunn, C. D. (2018). Socioeconomic gaps in science achievement. International Journal of STEM Education, 5, 38. pdf
  27. Guerra, J., Schunn, C. D., Bull, S., Barria-Pineda, J., & Brusilovsky, P. (2018). Navigation support in complex open learner models: Assessing visual design alternatives. New Review of Hypermedia and Multimedia, 3, 160-192. pdf
  28. Mandala, M., Schunn, C. D., Dow, S., Goldberg, M., & Perlman, J. (2018). Uncovering the practices, challenges, and incentives for engineering design faculty. International Journal of Engineering Education, 34(4), 1314-1324. pdf
  29. Pareja Roblin, N., Schunn, C., Bernstein, D., & McKenney, S. (2018). Exploring shifts in the characteristics of US government-funded science curriculum materials and their (unintended) consequences. Studies in Science Education, 54(1), 1-39. pdf
  30. Schunn, C. D., Newcombe, N., Alfieri, L., Cromley, J., Massey, C., & Merlino, J. (2018). Using principles of cognitive science to improve science learning in middle school: What works when and for whom? Applied Cognitive Psychology, 32, 225-240. pdf
  31. Witherspoon, E., Higashi, R., Schunn, C. D., & Shoop, R. (2018). Attending to structural programming features predicts differences in learning and motivation in a virtual robotics programming curriculum. Journal of Computer Assisted Learning, 34(2), 115-128. pdf
  32. Pareja Roblin, N., Schunn, C., & McKenney, S. (2018). What Are Critical Features of Science Curriculum Materials that Impact Student and Teacher Outcomes? Science Education, 102(2), 260-282. pdf
  33. Cannady, M., Moore, D., Votruba-Drzal, E., Greenwald, E., Stites, R. & Schunn, C. (2017). How personal, behavioral, and environmental factors predict working in STEMM vs non-STEMM Middle-Skill Careers. International Journal of STEM Education, 4:22. pdf
  34. Tekkumru-Kisa, M., Stein, M. K., & Schunn, C. D., (in press). Identifying cognitively demanding science tasks to provide opportunities for students to engage in three-dimensional learning. The Science Teacher. pdf
  35. Malone, K. L., Schunn, C. D., & Schuchardt, A. (2018). Improving conceptual understanding and representation skills through Excel-based modeling. Journal of Science Education and Technology, 27(1), 30-44. pdf
  36. Schuchardt, A., Tekkumru-Kisa, M., Schunn, C. D., Stein, M. K., & Reynolds, B. (2017). How much professional development is needed with educative curriculum materials? It depends upon the intended student learning outcomes. Science Education, 101, 1015–1033. pdf
  37. Witherspoon, B., Higashi, R. M., Schunn, C. D., Baehr, E. C. & Shoop, R. (2017). Developing computational thinking through a Virtual Robotics Programming Curriculum. ACM Transactions on Computing Education, 18(1). pdf
  38. Menekse, M., Higashi, R., Schunn, C., & Baehr, E. (2017). Exploring the role of robotics teams’ collaboration quality on team performance in a robotics tournamen. Journal of Engineering Education, 106(4), 564-584. pdf
  39. Barstow, B., Fazio, L., Schunn, C. D., & Ashley, K. (2017). Experimental evidence for diagramming benefits in science writing. Instructional Science, 45(5), 537-556. 10.1007/s11251-017-9415-3 pdf
  40. Liu, A.S., & Schunn, C. D. (2017). Applying math onto mechanisms: mechanistic knowledge is associated with the use of formal mathematical strategies. Cognitive Research, 2(6). pdf
  41. Barstow, B., Fazio, L., Lippman, J., Falakmasir, M., Schunn, C., & Ashley, K. (2017). The impacts of domain-general vs. domain-specific diagramming tools on writing. International Journal of Artificial Intelligence in Education, 27(4), 671-693.pdf
  42. Schunn, C. D. (2017). Building from in vivo research to the future of research on relational thinking and learning. Educational Psychology Review, 29(1), 97-104.pdf
  43. Witherspoon, E. B., Schunn, C. D., Higashi, R. M., & Baehr, E. C. (2016). Gender, interest and prior experience shape opportunities to learn programming in robotics competitions. International Journal of STEM Education, 3(18). pdf
  44. Cromley, J. M., Weisberg, S. M., Dai, T., Newcombe, N. S., Schunn, C. D., Massey, C., & Merlino, F. J. (2016). Improving middle school science learning using diagrammatic reasoning, Science Education, 100(6), 1184-1213. pdf
  45. Cox, C., Apedoe, X., Silk, E., & Schunn, C. D. (2017). Analyzing materials in order to find design opportunities for the classroom. In S. Goldman & Z. Kabayadondo (Eds.), Taking Design Thinking to School. pdf
  46. Iriti, J., Bickel, W., Schunn, C., & Stein, M. K. (2016). Maximizing research and development resources: Identifying and testing “load-bearing conditions” for educational technology innovations. Educational Technology Research & Development, 64, 245-262. 10.1007/s11423-015-9409-2 pdf
  47. Cox, C., Reynolds, B., Schuchardt, A., & Schunn, C. D., (2016). How do secondary level biology teachers make sense of using mathematics in design-based lessons about a biological process? In L. Annetta & J. Minogue (Eds.), Connecting Science and Engineering Practices in Meaningful Ways (pp. 339-372). Heidelberg: Springer. pdf
  48. Cox, C., Schuchardt, A., Reynolds, B., & Schunn, C. D. (2016). Using mathematics and engineering to solve problems in secondary level biology. Journal of STEM Education: Innovations and Research, 17(1), 6-14. >pdf
  49. Schuchardt, A., & Schunn, C. D. (2016). Modeling scientific processes with mathematics equations enhances student qualitative conceptual understanding and quantitative problem solving. Science Education, 100(2), 290–320. pdf
  50. Crowell, A. J., & Schunn, C. D. (2016). Unpacking the relationship between science education and applied scientific literacy. Research in Science Education, 46(1), 129-140. 10.1007/s11165-015-9462-1. pdf
  51. Bathgate, M.E., Crowell, A.J., Cannady, M., Dorph, R. & Schunn, C.D. (2015). The learning benefits of being willing and able to engage in scientific argumentation. International Journal of Science Education, 37(10), 1590-1612. 10.1080/09500693.2015.1045958 pdf
  52. Kessler, A., Stein, M. K., & Schunn, C. (2015). Cognitive demand of model tracing tutor tasks: Conceptualizing and predicting how deeply students engage. Technology, Knowledge and Learning, 20(3), 317-337. pdf
  53. Alfieri, L., Higashi, R., Shoop, R., & Schunn, C. D. (2015). Case studies of a robot-based game to shape interests and hone proportional reasoning skills. International Journal of STEM Education, 2:4. pdf
  54. Peffer, M. E., Beckler, M. L., Schunn, C. D., Renken, M., & Revak, A. (2015). Science classroom inquiry (SCI) simulations: A novel method to scaffold science learning. PLoS ONE, 10(3): e0120638. link
  55. Tekkumru-Kisa, M., Stein, M. K., & Schunn, C. D. (2015). A framework for analyzing cognitive demand and content-practices integration: Task analysis guide in science. Journal of Research in Science Teaching, 52(5), 659-685. pdf
  56. Crowell, A. J., & Schunn, C. D. (2014). The context-specificity of scientifically literate action: key barriers and facilitators across contexts and contents. Public Understanding of Science, 23(6), 718-733. pdf
  57. Cox, C., Reynolds, B., Schuchardt, A., & Schunn, C. D., (2014). How do secondary level biology teachers make sense of using mathematics in engineering design-based lessons about describing and predicting a biological process? In L. Annetta & J. Minogue (Eds.), Achieving science and technological literacy through engineering design practices. Springer.
  58. Liu, A., Schunn, C. D., Flot, J., & Shoop, R. (2013). The role of physicality in rich programming environments. Computer Science Education, 23(4), 315-331. pdf
  59. Apedoe, X. & Schunn, C. D. (2013). Strategies for Success: Uncovering what makes students successful in design and learning. Instructional science, 41, 773-791. pdf
  60. Apedoe,X. & Ellefson, M. E., Schunn, C. D. (2012). Learning together while designing: Does group size make a difference? Journal of Science Education and Technology, 21(1), 83-94. pdf
  61. Schunn, C. D., Silk, E. M., & Apedoe, X. S. (2012). Engineering in/&/or/for science education. In S. M. Carver and J. Shrager (Eds.), From Child to Scientist. Washington, DC: APA Press. pdf
  62. Schunn, C.D., & Silk, E. M. (2011). Learning theories for engineering technology and engineering education. In M. Barak and M. Hacker (Eds.), Fostering Human Development through Engineering and Technology Education (p. 3–18). Sense Publishers. pdf
  63. Singh, C., Moin, L., & Schunn, C. D. (2010). Introduction to physics teaching for science and engineering undergraduates. Journal of Physics Teacher Education Online, 5(3), 3-10. pdf
  64. Silk, E. M., Higashi, R., Shoop, R., Schunn, C. D. (2010). Designing technology activities that teach mathematics. The Technology Teacher, 69(4), 21-27. pdf
  65. Doppelt, Y. , Schunn, C. D., Silk, E., Mehalik, M., Reynolds, B., & Ward, E. (2009). Evaluating the impact of a facilitated learning community approach to professional development on teacher practice and student achievement. Research in Science & Technological Education, 27(3), 339-354.pdf
  66. Singh, C., & Schunn, C. D. (2009). Connecting three pivotal concepts in K-12 science state standards and maps of conceptual growth to research in physics education. Journal of Physics Teacher Education Online, 5(2), 16-28. pdf
  67. Steinberg, D., Patchan, M., Schunn, C. D., Landis, A. (2009). Determining adequate information for green building occupant training materials. Journal of Green Building, 4(3), 143-150. pdf
  68. Steinberg, D., Patchan, M., Schunn, C. D., Landis, A. (2009). Developing a focus for green building occupant training materials. Journal of Green Building, 4(2), 175–184.pdf
  69. Silk, E. M., Schunn, C. D., & Shoop, R. (2009). Synchronized robot dancing: Motivating efficiency and meaning in problem solving with robotics. Robot Magazine, 17, 42-45. pdf
  70. Schunn, C. D. (2009). How Kids Learn Engineering: The Cognitive Science Perspective. The Bridge, 39(3), 32-37. pdf (in) Hebrew)
  71. Silk, E., Schunn, C. D., & Strand-Cary, M. (2009). The impact of an engineering design curriculum on science reasoning in an urban setting. Journal of Science Education and Technology, 18(3), 209-223. pdf
  72. Reynolds, B., Mehalik, M. M., Lovell, M. R., & Schunn, C. D. (2009).Increasing student awareness of and interest in engineering as a career option through design-based learning. International Journal of Engineering Education, 25(1), 788-798. pdf
  73. Schunn, C. D. (2008). Engineering educational design. Educational Designer, 1. html (in Hebrew)
  74. Apedoe, X., Reynolds, B., Ellefson, M. R., & Schunn, C. D. (2008). Bringing engineering design into high school science classrooms: The heating/cooling unit. Journal of Science Education and Technology, 17(5), 454–465. pdf
  75. Doppelt, Y. & Schunn, C. D. (2008). Identifying students' perceptions of the important classroom features affecting learning aspects of a design based learning environment? Learning Environments Research, 11(3), 195-209. pdf
  76. Ellefson,M., Brinker, R., Vernacchio, V., & Schunn, C. D.(2008). Design-based learning for biology: Genetic engineering experience improves understanding of gene expression. Biochemistry and Molecular Biology Education, 36(4), 292–298. pdf
  77. Doppelt, Y., Mehalik, M. M., Schunn, C. D., & Krysinski, D. (2008). Engagement and achievements in design-based learning. Journal of Technology Education, 19(2), 21-38. pdf
  78. Mehalik, M. M., & Doppelt, Y., & Schunn, C. D. (2008). Middle-school science through design-based learning versus scripted inquiry: Better overall science concept learning and equity gap reduction. Journal of Engineering Education, 97(1), 71-85. pdf
  79. Moin, L., Dorfield, J., & Schunn, C. D. (2005). Where Can We Find Future K-12 Science & Math Teachers? A Search by Academic Year, Discipline, and Achievement Level. Science Education, 89, 980-1006. pdf
  80. Schunn, C. D., & Anderson, J. R. (2001). Science education in universities: Explorations of what, when, and how. In K. Crowley, C.D. Schunn, & T. Okada (Eds.), Designing for Science: Implications from Professional, Instructional, and Everyday Science. Mawah, NJ: Erlbaum. pdf

Web-Based Peer Interaction

    Featured
  1. Yu, Q. & Schunn, C. D. (2023). Understanding the what and when of peer feedback benefits for performance and transfer. Computers in Human Behavior, 147, 107857. 10.1016/j.chb.2023.107857 pdf
  2. Wu, Y. & Schunn, C. D. (2023). Passive, active, and constructive engagement with peer feedback A revised model of learning from peer feedback. Contemporary Educational Psychology, 73, 102160. 10.1016/j.cedpsych.2023.102160 pdf
  3. Wu, Y. & Schunn. C.D. (2021). The effects of providing and receiving peer feedback on writing performance and learning of secondary school students. American Educational Research Journal, 58(3), 492-526. pdf
  4. Schunn, C. D., Godley, A. J., & DiMartino, S. (2016). The reliability and validity of peer review of writing in high school AP English classes. Journal of Adolescent & Adult Literacy. 60(1), 13–23. pdf
  5. Schunn, C. D. (2016). Writing to learn and learning to write through SWoRD. In S.A. Crossley & D.S. McNamara (Eds.), Adaptive Educational Technologies for Literacy Instruction. NY: Taylor & Francis, Routledge. pdf
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  6. Zhang, F., Schunn, C. D. & Wu, Y. (In press). What does it mean to be good at peer reviewing?: A multidimensional scaling and cluster analysis study of behavioral indicators of peer feedback literacy. International Journal of Educational Technology in Higher Education.
  7. Zhao, Y., Zhang, F., Schunn, C. D., He, P., Li, D., & Zhao, Y. (In press). Feedback, feedback-on-feedback and re-feedback: Effects of written dialogic peer feedback on EFL writing. Assessment & Evaluation in Higher Education. 10.1080/02602938.2023.2278017 pdf
  8. Kiselyov, K. & Schunn. C.D. (2023). Peer-reviewed presentation exchange in an undergraduate classroom. Journal of Microbiology and Biology Education, e00067-23. 10.1128/jmbe.00067-23 pdf
  9. Xiong, Y., Schunn. C.D. & Wu, Y. (2023). What predicts variation in reliability and validity of online peer assessment?: A large-scale cross-context study. Journal of Computer Assisted Learning, 39(6), 2004-2024. 10.1111/jcal.12861 pdf
  10. Zong, Z., & Schunn, C. D. (2023). Does matching peers at finer-grained levels of prior performance enhance gains in task performance from peer review? International Journal of Computer-Supported Collaborative Learning, 18, 425–456. 10.1007/s11412-023-09401-4 pdf
  11. Zong, Z., Schunn, C. D., & Wang, Y. (2023). When do students provide more peer feedback? The roles of performance and prior feedback experiences. Instructional Science, 51, 977–1003. 10.1007/s11251-023-09640-w pdf
  12. Dong, Z., Gao, Y., & Schunn, C. D. (2023). Assessing students’ peer feedback literacy in writing: Scale development and validation. Assessment & Evaluation in Higher Education, 48(8), 1103-1118. 10.1080/02602938.2023.2175781 pdf
  13. Zhang, Y., & Schunn, C. (2023). Self-regulation of peer feedback quality aspects through different dimensions of experience within prior peer feedback assignments. Contemporary Educational Psychology, 74, 102210. pdf
  14. Yu, Q. & Schunn, C. D. (2023). Understanding the what and when of peer feedback benefits for performance and transfer. Computers in Human Behavior, 147, 107857. 10.1016/j.chb.2023.107857 pdf
  15. Zhang, F., Schunn, C., Chen, S., Li, W., & Li, R. (2023). EFL student engagement with giving peer feedback in academic writing: A longitudinal study. Journal of English for Academic Purposes , 64, 101255. pdf
  16. Gao, Y., An, Q., & Schunn, C. D. (2023). The bilateral benefits of providing and receiving peer feedback in academic writing across varying L2 proficiency, Studies in Educational Evaluation, 77, 101252. 10.1016/j.stueduc.2023.101252 pdf
  17. Wu, Y. & Schunn, C. D. (2023). Passive, active, and constructive engagement with peer feedback A revised model of learning from peer feedback. Contemporary Educational Psychology, 73, 102160. 10.1016/j.cedpsych.2023.102160 pdf
  18. Wu, Y. & Schunn, C. D. (2023). Assessor writing performance on peer feedback: Exploring the relation between assessor writing performance, problem identification accuracy, and helpfulness of peer feedback. Journal of Educational Psychology, 115(1), 118–142. 10.1037/edu0000768 pdf
  19. Tong, Y., Schunn, C. D., & Wang, H. (2023). Why increasing the number of raters only helps sometimes: Reliability and validity of peer assessment across tasks of different complexity. Studies in Educational Evaluation, 76, 101233. pdf
  20. Cui, Y., Schunn, C. D., & Gai, X. (2022). Peer feedback and teacher feedback: A comparative study of revision effectiveness in writing instruction for EFL learners. Higher Education Research & Development, 41(6), 1838-1854. pdf
  21. Zong, Z., Schunn, C. D., & Wang, Y. (2022). What makes students contribute more peer feedback? The role of within-course experience with peer feedback. Assessment & Evaluation in Higher Education, 47(6), 972-983. pdf
  22. Zong, Z., Schunn, C. D., & Wang, Y. (2022). Do experiences of interactional inequality predict lower depth of future student participation in peer review? Computers in Human Behavior, 127, 107056. pdf
  23. Zong, Z., Schunn, C. D., & Wang, Y. (2021). Learning to improve the quality peer feedback through experience with peer feedback. Assessment & Evaluation in Higher Education, 46(6), 973-992. pdf
  24. Cui, Y., Schunn, C. D., Gai, X., Jiang, Y., & Wang, Z. (2021). Effects of trained peer vs. teacher feedback on EFL students’ writing performance, self-efficacy, and internalization of motivation. Frontiers in Psychology, 12, 6659. html
  25. Zong, Z., Schunn, C. D., & Wang, Y. (2021). What aspects of online peer feedback robustly predict growth in students’ task performance. Computers in Human Behavior, 124, 106924. pdf
  26. Xiong, X. & Schunn. C.D. (2021). Reviewer, Essay, and Reviewing Process Characteristics that Predict Errors in Web-based Peer Review. Computers & Education, 166, 104146. pdf
  27. Wu, Y. & Schunn. C.D. (2021). From plans to actual implementation: A process model for why feedback features influence feedback implementation. Instructional Science, 49(3), 365-394. pdf
  28. Wu, Y. & Schunn. C.D. (2021). The effects of providing and receiving peer feedback on writing performance and learning of secondary school students. American Educational Research Journal, 58(3), 492-526. pdf
  29. Wu, Y. & Schunn. C.D. (2020). When peers agree, do students listen? The central role of feedback quality and feedback frequency in determining uptake of feedback. Contemporary Educational Psychology, 62, 101897 pdf
  30. Wu, Y. & Schunn. C.D. (2020). From feedback to revisions: Effects of feedback features and perceptions. Contemporary Educational Psychology, 60, 101826. pdf
  31. Zhang, F., Schunn, C. D., Li, W., & Long, M. (2020). Changes in the reliability and validity of peer assessment across the college years. Assessment and Evaluation in Higher Education, 45(8), 1073-1087. pdf
  32. Schunn, C. D., & Wu, Y (2019). The learning science of multi-peer feedback for EFL students. Technology Enhanced Foreign Language Education, 189, 13-21. pdf
  33. Gao. Y., Wang, Y., & Schunn. C.D. (2019). Implementation of peer feedback and its potential mediators in English writing. Technology Enhanced Foreign Language Education, 186(2), 17-24. pdf
  34. Elizondo-Garcia, J., Schunn. C.D., & Gallardo, K. (2019). Quality of peer feedback in relation to instructional design: A comparative study in energy and sustainability MOOCs. International Journal of Instruction, 12(1), 1308-1470. pdf
  35. Gao. Y., Schunn. C.D., & Yu, Q. (2018). The alignment of written peer feedback with draft problems and its impact on revision in peer assessment. Assessment & Evaluation in Higher Education, 44(2), 294-308. pdf
  36. Mandala, M., Schunn, C. D., Dow, S., Goldberg, M., Perlman, J., Clark, W., & Mena, I. (2018). Collaborative team peer review generation improves feedback quality and reviewer engagement. International Journal of Engineering Education, 34(4), 1299-1313. pdf
  37. Cho, K., & Schunn, C.D. (2018). Finding an optimal balance between agreement and performance in an online reciprocal peer evaluation system. Studies in Educational Evaluation, 56, 94–101. pdf
  38. Zou, M., Schunn, C., Wang, Y., & Zhang, F. (2018). Student Attitudes That Predict Participation in Peer Assessment. Assessment & Evaluation in Higher Education, 43(5), 800-811. pdf
  39. Patchan, M. M., Schunn, C.D., & Clark, R. (2018). Accountability in peer assessment: examining the effects of reviewing grades on peer ratings and peer feedback. Studies in Higher Education, 43(12), 2263-2278. pdf
  40. Zhang, F., Schunn, C.D., & Baikadi, A. (2017). Charting the routes to revision: An interplay of writing goals, peer comments, and self-reflections from peer review. Instructional Science, 45(5), 679-707. pdf
  41. Patchan, M. M., Schunn, C. D., & Correnti, R. (2016). The nature of feedback: how feedback features affect students' implementation rate and quality of revisions. Journal of Educational Psychology, 108(8), 1098-1120.pdf
  42. Schunn, C. D., Godley, A. J., & DiMartino, S. (2016). The reliability and validity of peer review of writing in high school AP English classes. Journal of Adolescent & Adult Literacy. 60(1), 13–23. pdf
  43. Schunn, C. D. (2016). Writing to learn and learning to write through SWoRD. In S.A. Crossley & D.S. McNamara (Eds.), Adaptive Educational Technologies for Literacy Instruction. NY: Taylor & Francis, Routledge. pdf
  44. Patchan, M. M., & Schunn, C. D. (2016). Understanding the effects of receiving peer feedback for text revision: relations between author and reviewer ability. Journal of Writing Research, 8(2), 227-265. pdf
  45. Patchan, M. M., & Schunn, C. D. (2015). Understanding the benefits of providing peer feedback: How students respond to peers' texts of varying quality. Instructional Science, 43(5), 591-614. 10.1007/s11251-015-9353-x. pdf
  46. Abramovich, S., Schunn, C., D., Correnti, R. J. (2013). The role of evaluative metadata in an online teacher resource exchange. Educational Technology Research & Development, 61, 863-883. pdf
  47. Abramovich, S., & Schunn, C. D. (2012). Studying teacher selection of resources in an ultra-large scale interactive system: Does metadata guide the way? Computers & Education, 58(1), 551-559. pdf
  48. Patchan, M. M., Hawk, B. H., Stevens, C. A., & Schunn, C. D. (2013). The effects of skill diversity on commenting and revisions. Instructional Science, 41(2), 381-405. pdf
  49. Xiong, W., Litman, D., & Schunn, C. D. (2012). Redesigning peer review interactions using computer tools. Journal of Writing Research, 4(2), 155-176. pdf
  50. Lee, C. J., & Schunn, C.D. (2011). Social biases and solutions for procedural objectivity. Hypatia, 26(2), 352-373. pdf
  51. Patchan, M. M., Schunn, C.D., & Clark, R. (2011). Writing in natural sciences: Understanding the effects of different types of reviewers on the writing process. Journal of Writing Research, 2(3), 365-393. pdf
  52. Kaufman, J. H., & Schunn, C. D. (2011). Students' perceptions about peer assessment for writing: Their origin and impact on revision work. Instructional Science, 39(3), 387-406. pdf
  53. Cho, K., & Schunn, C. D. (2010). Developing writing skills through students giving instructional explanations. In M. K. Stein & L. Kucan (Eds.), Instructional Explanations in the Disciplines: Talk, Texts and Technology. New York: Springer. pdf
  54. Patchan, M. M., Charney, D., & Schunn, C. D. (2009). A validation study of students’ end comments: Comparing comments by students, a writing instructor, and a content instructor. Journal of Writing Research, 1(2), 124-152. pdf
  55. Nelson, M. M., & Schunn, C. D. (2009). The nature of feedback: How different types of peer feedback affect writing performance. Instructional Science, 27(4), 375-401. pdf
  56. Cho, K., Chung, T. R., King, W. R., & Schunn,C. D. (2008). Peer-based computer-supported knowledge refinement: An empirical investigation. Communications of the ACM, 51(3), 83-88. pdf
  57. Cho, K., & Schunn, C. D. (2007). Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system. Computers and Education, 48(3), 409-426. pdf
  58. Cho, K., Schunn, C. D., & Charney, D. (2006). Commenting on writing: Typology and perceived helpfulness of comments from novice peer reviewers and subject matter experts. Written Communication, 23(3), 260-294. pdf
  59. Cho, K., Schunn, C. D., & Wilson, R. (2006). Validity and reliability of scaffolded peer assessment of writing from instructor and student perspectives. Journal of Educational Psychology, 98(4), 891-901. pdf

Neuroscience of Complex Learning

    Featured
  1. Liu, R., Schunn, C. D., Fiez, J., A., & Libertus, M. E. (2018). The integration between non-symbolic and symbolic numbers: Evidence from an EEG study. Brain & Behavior, 8(4), e00938. pdf
  2. Wong, A., Moss, J., & Schunn, C. D. (2016). Tracking reading strategy utilization through pupillometry. Australasian Journal of Educational Technology, 32(6). pdf
  3. Kallai, A. Y., Schunn, C. D., & Fiez, J. A. (2012). Mental arithmetic activates analogic representations of internally generated sums. Neuropsychologia, 50, 2397–2407. pdf
    4. All
  4. Liu, R., Schunn, C. D., Fiez, J., A., & Libertus, M. E. (2018). The integration between non-symbolic and symbolic numbers: Evidence from an EEG study. Brain & Behavior, 8(4), e00938. pdf
  5. Wong, A., Moss, J., & Schunn, C. D. (2016). Tracking reading strategy utilization through pupillometry. Australasian Journal of Educational Technology, 32(6). pdf
  6. Moss, J., & Schunn, C. D. (2015). Comprehension through explanation as the interaction of the brain’s coherence and cognitive control networks. Frontiers in Human Neuroscience, 9(562). doi: 10.3389/fnhum.2015.00562 link
  7. Liu, A. S., Kallai, A. Y., Schunn, C. D., & Fiez, J. (2015). Using mental computation training to improve complex mathematical performance. Instructional Science, 43(4), 463-485. 10.1007/s11251-015-9350-0 pdf
  8. Richey, J. E., Phillips, J. S., Schunn, C. D., & Schneider, W. (2014). Is the link from working memory to analogy causal? No analogy improvements following working memory training gains. PLOS One, 9(9), e106616. pdf
  9. Moss, J., Schunn, C.D., Schneider, W., McNamara, D. S. (2013). The nature of mind wandering during reading varies with the cognitive control demands of the reading strategy. Brain Research, 1539, 48-60. pdf
  10. Kallai, A. Y., Schunn, C. D., & Fiez, J. A. (2012). Mental arithmetic activates analogic representations of internally generated sums. Neuropsychologia, 50, 2397–2407. pdf
  11. Verstynen, T., Phillps, J., Braun, E., Schneider, W., & Schunn, C. D. (2012). Dynamic sensorimotor planning during long-term sequence learning: the role of variability, response chunking and planning errors. PLoS ONE, 7(10), e47336. pdf
  12. Moss, J., Schunn C.D., Schneider, W., McNamara, D. S., & VanLehn, K. (2011). The neural correlates of strategic reading comprehension: cognitive control and discourse comprehension. NeuroImage, 58(2), 675-686. pdf

Engagement & Learning

    Featured
  1. Witherspoon, E. & Schunn, C. D. (2020). Locating and understanding the largest gender differences in pathways to science degrees. Science Education, 104(2), 144-163. pdf
  2. Whitcomb, K. M., Kalender, Z. Y., Nokes-Malach, T. J., Schunn, C. D., & Singh, C. (2020). Comparison of self-efficacy and performance of engineering undergraduate women and men. International Journal of Engineering Education 36(6), 1996–2014. pdf
  3. Witherspoon, E., Vincent-Ruz, P., & Schunn, C. D. (2019). When making the grade isn't enough: The gendered nature of pre-med science course attrition. Educational Researcher, 48(4), 193-204. pdf
  4. Liu, A. S., & Schunn, C. D. (2018). The effects of school-related and home-related optional science experiences on science attitudes and knowledge. Journal of Educational Psychology, 110(6), 798-810. pdf
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  5. Rubio, J. W., Schunn, C.D., & Castleman, S. E. (in press). When my teacher speaks Spanish, my math classroom experience changes: Tracking attitudinal and achievement effects. Language, Culture and Curriculum. pdf
  6. Menzies, C. & Schunn, C. (in press). Designing for equity at scale. Educational Designer.
  7. Malespina, A., Schunn, C., & Singh, C. (2023). Bioscience students’ internalized mindsets predict grades and reveal gender inequities in physics courses. Physical Review Physics Education Research, 19, 020135. 10.1103/PhysRevPhysEducRes.19.020135 pdf
  8. Dorph, R., Cannady, M., & Schunn, C. D. (2022). What drives visitor engagement in exhibits? The interaction between visitor activation profiles and exhibit features. Curator, 65(2), 399-416. pdf
  9. Malespina, A., Schunn, C., & Singh, C. (2022). Whose ability and growth matter: Gender, mindset and performance in physics. International Journal of STEM Education, 9, 28. html
  10. Kalender, Z. Y., Marshman, E., Schunn, C., Nokes-Malach, T., & Singh, C. (2022). Framework for unpacking gendered mindsets in physics by students’ gender. Physical Review Physics Education Research, 18, 010116. pdf
  11. Witherspoon, E. & Schunn, C. D. (2022). Sources of gender differences in competency beliefs and retention in an introductory pre-medical science course. Journal of Research Science in Teaching, 59(5), 695-719. pdf
  12. Vincent-Ruz, P. & Schunn, C. D. (2021). Identity complexes and science identity in early secondary: Mono-topical or in combination with other topical identities. Research in Science Education, 51, 369-390. pdf
  13. Whitcomb, K. M., Kalender, Z. Y., Nokes-Malach, T. J., Schunn, C. D., & Singh, C. (2020). Comparison of self-efficacy and performance of engineering undergraduate women and men. International Journal of Engineering Education 36(6), 1996–2014. pdf
  14. Higashi, R. M., & Schunn, C. D. (2020). Perceived relevance of digital badges predicts longitudinal change in program engagement, Journal of Educational Psychology. 12(5), 1020–1041. pdf
  15. Blatt, L.R., Schunn, C. D., Votruba-Drzal, E., & Rottman, B. M. (2020). Variation in which key motivational and academic resources relate to academic performance disparities across introductory college courses. International Journal of STEM Education, 7, 58. pdf
  16. Kalender, Z. Y., Marshman, E., Schunn, C., Nokes-Malach, T. & Singh, C. (2020). Damage caused by women’s lower self-efficacy on physics learning. Physical Review Physics Education Research, 16(1), 010118. pdf
  17. Witherspoon, E. & Schunn, C. D. (2020). Locating and understanding the largest gender differences in pathways to science degrees. Science Education, 104(2), 144-163. pdf
  18. Bodnar, K., Hofkens, T. L., Wang, M.-T., & Schunn, C. D. (2020). Science identity predicts science career aspiration across gender and race, but especially for boys. Journal of Gender, Science and Technology, 12(1), 32-45. pdf
  19. Kalender, Z. Y., Marshman, E., Schunn, C., Nokes-Malach, T. & Singh, C. (2019). Why female science, technology, engineering, and mathematics majors do not identify with physics: They do not think others see them that way Physical Review Physics Education Research, 15(2), 020148. pdf
  20. Kalender, Z. Y., Marshman, E., Schunn, C., Nokes-Malach, T. & Singh, C. (2019). Gendered patterns in the construction of physics identity from motivational factors? Physical Review Physics Education Research, 15(2), 020119. pdf
  21. Bonnette, R., Schunn, C. D., & Crowley, K. (2019). Falling in love and staying in love with science: Ongoing informal science experiences support fascination for all children. International Journal of Science Education, 41(12), 1626-1643. pdf
  22. Witherspoon, E., Vincent-Ruz, P., & Schunn, C. D. (2019). When making the grade isn't enough: The gendered nature of pre-med science course attrition. Educational Researcher, 48(4), 193-204. pdf
  23. Marshman, E., Kalender, Z. Y., Nokes-Malach, T., Schunn, C., & Singh, C. (2018). Female students with A’s have similar physics self-efficacy as male students with C’s in introductory courses: A cause for alarm? Physical Review Physics Education Research, 14, 020123. pdf
  24. Vincent-Ruz, P., & Schunn, C. D. (2018). The nature of science identity and its role as driver of student choices. International Journal of STEM Education, 5, 48. pdf
  25. Ben-Eliyahu, A., Moore, D., Dorph, R., & Schunn, C. D. (2018). Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement in science across multiple days, activities, and contexts. Contemporary Educational Psychology, 53, 87-105. pdf
  26. Vincent Ruz, P., Grabowski, J., & Schunn, C. D. (2018). The impact of early participation in undergraduate research experiences on multiple measures of pre-med path success. SPUR: Scholarship and Practice of Undergraduate Research, 1(3), 13-18. pdf
  27. Dorph, R. & Schunn, C. D. (2018). Activating Jewish learners: Positioning youth for persistent success in Jewish learning and living. In J. Levisohn and J. Kress (Eds.), Advancing the Learning Agenda in Jewish Education. Brighton, MA. Academic Studies Press. pdf
  28. Vincent Ruz, P., Binning, K., Schunn, C. D., & Grabowski, J. (2018). The effect of math SAT on women’s chemistry competency beliefs. Chemistry Education Research and Practice, 1, 342-351. pdf
  29. Liu, A. S., & Schunn, C. D. (2018). The effects of school-related and home-related optional science experiences on science attitudes and knowledge. Journal of Educational Psychology, 110(6), 798-810. pdf
  30. Dorph, R., Bathgate, M.E., Schunn, C. D., & Cannady, M. (2018). When I grow up: The relationship of science learning activation to STEM career preference. International Journal of Science Education, 40(9), 1034-1057. pdf
  31. Bathgate, M. E., & Schunn, C. D. (2017). The psychological characteristics of experiences that influence science motivation and content knowledge. International Journal of Science Education, 17, 2402-2432. pdf
  32. Akiva, T., Schunn, C., & Louw, M. (2017). What drives attendance at informal learning activities?: A study of two art programs. Curator: The Museum Journal, 60(3), 351-364. pdf
  33. Marshman, E., Kalender, Z. Y., Schunn, C. D., Nokes-Malach, T., & Singh, C. (2018). A longitudinal analysis of underrepresented students’ motivational characteristics in introductory physics courses. Canadian Journal of Physics. pdf
  34. Higashi, R. M., Schunn, C. D., & Flot, J. B. (2017). Different underlying motivations and abilities predict students’ versus teachers' persistence in an online course. Educational Technology Research and Development, 65(6), 1471-1493. pdf
  35. Bathgate, M. E., & Schunn, C. D. (2017). Factors that deepen or attenuate decline of science utility value during the middle school years. Contemporary Educational Psychology, 49, 215–225. pdf
  36. Vincent Ruz, P., & Schunn, C. D. (2017). The increasingly important role of science competency beliefs for science learning in girls. Journal of Research in Science Teaching, 54(6), 790–822. pdf
  37. Dorph, R., Schunn, C. D., & Crowley, K. (2017). Crumpled molecules and edible plastic: science learning activation in out of school time. Afterschool Matters, 25, 18-28. pdf
  38. Akiva, T., Kehoe, S., & Schunn, C. D. (2017). Are we ready for citywide learning? Examining the nature of within- and between-program pathways in a community-wide learning initiative. Journal of Community Psychology, 45(3), 413-425. pdf
  39. Lin, P.-Y. & Schunn, C. D. (2016). The dimensions and impact of informal science learning experiences on middle schoolers’ attitudes and abilities in science. International Journal of Science Education, 38(17), 2551-2572. pdf
  40. Dorph, R., Cannady, M., & Schunn, C. D. (2016). How science learning activation enables success for youth in science learning. Electronic Journal of Science Education, 20(8). pdf
  41. Bathgate, M. E., & Schunn, C. D. (2016). Disentangling intensity from breadth of science interest: What predicts learning behaviors? Instructional Science, 44(5), 423-440. pdf
  42. Sha, L., Schunn, C. D., Bathgate, M., & Ben-Eliyahu, A. (2016). Families support their children’s success in science learning by influencing interest and self-efficacy. Journal of Research in Science Teaching, 53(3), 450–472. 10.1002/tea.21251. pdf
  43. Sha, L., Schunn, C. D. & Bathgate, M. (2015). Measuring choice to participate in optional science learning experiences during early adolescence. Journal of Research in Science Teaching, 52(5), 686-709. 10.1002/tea.21210 pdf
  44. Bathgate, M., Schunn, C. D., Correnti, R. J. (2014). Children’s motivation towards science across contexts, manner-of-interaction, and topic. Science Education, 98(2), 189-215. pdf
  45. Abramovich, S., Schunn, C. D., & Higashi, R. M (2013). Are badges useful in education?: It depends upon the type of badge and type of learner. Educational Technology Research & Development, 61(2), 217-232. pdf
  46. Bathgate, M., Schunn, C. D. (2013). Exploring and encouraging metacognitive awareness in novice music students. In M. Stakelum (Ed.), Developing the Musician, SEMPRE Studies in the Psychology of Music series. Ashgate.
  47. Bathgate, M. & Sims-Knight, J., & Schunn, C. D. (2012). Thoughts on thinking: Engaging novice music students in metacognition. Applied Cognitive Psychology, 26(3), 403-409. pdf

Human Computer Interaction & Learning

    Featured
  1. Jang, J., & Schunn, C. D. (2014). A Framework for Unpacking Cognitive Benefits of Distributed Complex Visual Displays. Journal of Experimental Psychology: Applied, 20(3), 260-269. pdf
  2. Jang, J., & Schunn, C. D. (2012). Physical design tools support and hinder innovative engineering design. Journal of Mechanical Design, 134(4), 041001-1-9. pdf
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  3. Baikadi, A., Demmans Epp, C., & Schunn, C. D. (2018). Participating by activity or by week in MOOCs. Information and Learning Science, 199(9/10), 572-585. pdf
  4. Patchan, M. M., Schunn, C. D., Sieg, W., & McLaughlin, D. (2016). The effect of blended instruction on accelerated learning. Technology, Pedagogy and Education, 25(3), 269-286. 10.1080/1475939X.2015.1013977 pdf
  5. Jang, J., & Schunn, C. D. (2014). A Framework for Unpacking Cognitive Benefits of Distributed Complex Visual Displays. Journal of Experimental Psychology: Applied, 20(3), 260-269. pdf
  6. Kirschenbaum, S. S., Schunn, C. D., & Trafton, J. G. (2014). Visualizing uncertainty: The impact on performance. Human Factors, 56(3), 509-520. pdf
  7. Jang, J., Trickett, S. B., Schunn, C. D., & Trafton, J. G. (2012). Unpacking the temporal advantage of distributing complex visual displays. International Journal of Human-Computer Studies, 70, 812-827. pdf
  8. Jang, J., & Schunn, C. D. (2012). Performance benefits of spatially distributed vs. stacked information on integration tasks. Applied Cognitive Psychology, 26, 207-214.pdf
  9. Jang, J., & Schunn, C. D. (2012). Physical design tools support and hinder innovative engineering design. Journal of Mechanical Design, 134(4), 041001-1-9. pdf
  10. Jang, J., Schunn, C.D., & Nokes, T. J. (2011). Spatially distributed instructions improve learning outcomes and efficiency. Journal of Educational Psychology, 103(1), 60-72. pdf
  11. MacWhinney, B., St. James, J., Schunn, C., Li, P., Schneider, W. (2001). STEP --- A system for teaching experimental psychology using E-Prime. Behavioral Research Methods, Instruments, & Computers, 33 (2), 287-296. pdf

Strategy Use & Learning

    Featured
  1. Schunn, C. D., McGregor, M., Saner, L. D. (2005). Expertise in ill-defined problem solving domains as effective strategy use. Memory & Cognition, 33(8), 1377-1387. pdf
  2. Schunn, C. D., & Reder, L. M. (2001). Another source of individual differences: Strategy adaptivity to changing rates of success. Journal of Experimental Psychology: General, 130(1), 59-76. pdf
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  3. Winsler, A., Abar, B., Feder, M., Rubio, D.A., & Schunn, C. D. (2007). Private speech and executive functioning among high functioning children with autistic spectrum disorders. Journal of Autism and Developmental Disabilities, 37(9), 1617-1635. pdf
  4. Hansberger, J. T., Schunn, C. D., & Holt, R. W. (2006). Strategy variability: How too much of a good thing can hurt performance. Memory & Cognition, 34(8), 1652-1666. pdf
  5. Schunn, C. D., McGregor, M., Saner, L. D. (2005). Expertise in ill-defined problem solving domains as effective strategy use. Memory & Cognition, 33(8), 1377-1387. pdf
  6. Morris, B. J., & Schunn, C. D. (2004). Rethinking logical reasoning skills from a strategy perspective. In M. J. Roberts & E. Newton (Eds.), Methods of thought: Individual differences in reasoning strategies. Psychology Press. pdf
  7. Schunn, C. D. (2002). Why motivation only sometimes affects base-rate sensitivity: The mediating role of representations on adaptive performance. In S. P. Shohov (Ed.), Advances in Psychology Research. New York: NovaScience. pdf
  8. Schunn, C. D., Lovett, M., & Reder, L. M. (2001). Awareness and working memory in strategy adaptivity. Memory & Cognition, 29(2), 256-266. pdf
  9. Schunn, C. D., & Reder, L. M. (2001). Another source of individual differences: Strategy adaptivity to changing rates of success. Journal of Experimental Psychology: General, 130(1), 59-76. pdf
  10. Lovett, M. C., & Schunn, C. D. (2000). The importance of frameworks for directing empirical questions: Reply to Goodie and Fantino. Journal of Experimental Psychology: General, 129(4), 453-456. pdf
  11. Lovett, M. C., & Schunn, C. D. (1999). Task representations, strategy variability and base-rate neglect. Journal of Experimental Psychology: General, 128(2), 107-130. pdf
  12. Reder, L. M., & Schunn, C. D. (1999). Bringing Together the psychometric and strategy worlds: Predicting adaptivity in a dynamic task. In D. Gopher & A. Koriat (Eds), Cognitive regulation of performance: Interaction of theory and application. Attention and Performance XVII.pdf
  13. Schunn, C. D., & Reder, L. M. (1998). Individual differences in strategy adaptivity. In D. L. Medin (Ed.), The psychology of learning and motivation. pdf
  14. Schunn, C. D., Reder, L. M., Nhouyvanisvong, A., Richards, D. R., & Stroffolino, P.J. (1997). To calculate or not calculate: A source activation confusion (SAC) model of problem-familiarity's role in strategy selection. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23(1), 3-29. pdf
  15. Reder, L., & Schunn, C. D. (1996). Metacognition does not imply awareness: Strategy choice is goverened by implicit learning and memory. In L. M. Reder (Ed.), Implicit memory and metacognition (pp. 45-78). Mahwah, NJ: Erlbaum. pdf

General Cognitive Science & Learning Science

    Featured
  1. Alfieri, L., Nokes, T. N., & Schunn, C. D. (2013). Learning through case comparisons: a meta-analytic review. Educational Psychologist, 48(2), 87-113. pdf
  2. Kong, X., Schunn, C. D., Wallstrom, G. L. (2010). High regularities in eye-movement patterns reveal the dynamics of visual working memory allocation mechanism. Cognitive Science, 34(2), 322-337. pdf
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  3. Schunn, C. D. (2019). What should cognitive science look like? Neither a tree nor physics. Topics in Cognitive Science, 11(4), 845-852. pdf
  4. Liu, A. S., & Schunn, C. D. (2016). The central questions of spatial cognition. In S. Chipman (Ed.), The Oxford Handbook of Cognitive Science. Oxford University Press. pdf
  5. Alfieri, L., Nokes, T. N., & Schunn, C. D. (2013). Learning through case comparisons: a meta-analytic review. Educational Psychologist, 48(2), 87-113. pdf
  6. Altmann, E. M. & Schunn, C. D. (2012). Decay versus Interference: A new look at an old interaction. Psychological Science, 23(11), 1435-1437. pdf
  7. Kong, X., Schunn, C. D., Wallstrom, G. L. (2010). High regularities in eye-movement patterns reveal the dynamics of visual working memory allocation mechanism. Cognitive Science, 34(2), 322-337. pdf
  8. Nokes, T. J., Schunn, C. D., & Chi, M. T. H. (2010). Problem solving and human expertise.In E. Baker, B. McGraw, & P. Peterson (Eds.), International Encyclopedia of Education, Third Edition. Oxford, UK: Elsevier. pdf
  9. Schunn, C. D. & Nelson, M. M. (2009). Expert-novice studies: An educational perspective. In Eric Anderman (Ed)Psychology of Classroom Learning: An Encyclopedia. Detroit, MI: Macmillan Reference. pdf
  10. Schunn, C. D., (2009). John Robert Anderson biography. In Eric Anderman (Ed.), Psychology of Classroom Learning: An Encyclopedia. Detroit, MI: Macmillan Reference.
  11. Kong, X., & Schunn, C. D. (2007). Global vs. local information processing in visual/spatial problem solving: The case of traveling salesman problem. Cognitive Systems Research, 8(3), 192-207. pdf
  12. Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. pdf
  13. Reder, L. M., Nhouyvansivong, A., Schunn, C. D., Ayers, M. S., Angstadt, P., Hiraki, K. (2000). A mechanistic account of the mirror effect for word frequency: A computational model of remember/know judgments in a continuous recognition paradigm. Journal of Experimental Psychology: Learning, Memory, & Cognition, 26 (2), 294-320. pdf
  14. Schunn, C. D., & Vera, A. H. (2004). Cross-cultural similarities in category structure. Thinking & Reasoning, 10(3), 273-287. pdf
  15. Schunn, C. D., & Gray, W. D. (2002). Introduction to the special issue on computational cognitive modeling. Cognitive Systems Research3, 1-3. pdf
  16. Duric, Z., Gray, W., Heishman, R., Li, F., Rosenfeld, A., Schoelles, M. J., Schunn, C., & Wechsler, H. (2002). Integrating perceptual and cognitive modeling for adaptive and intelligent human-computer interaction. Proceedings of the IEEE. 90(7), 1272-1289. pdf
  17. Anderson, J. R., & Schunn, C. D. (2000). Implications of the ACT-R learning theory: No magic bullets. In R. Glaser (Ed.), Advances in instructional psychology: Educational design and cognitive science, Vol. 5 (pp. 1-33). Mahwah, NJ: Erlbaum. pdf
  18. Schunn, C. D., & Klahr, D. (1998). Stances: Production systems. In W. Bechtel and G. Graham (Eds.), A Companion to Cognitive Science. Blackwell. pdf
  19. Schunn, C. D., & Vera, A. H. (1995). Causality and the categorization of objects and events. Thinking & Reasoning, 1(3), 237-284. Full paper

books

  1. Derry, S. J., Schunn, C. D., & Gernsbacher, M. A. (Eds.), (2005). Interdisciplinary Collaboration: An Emerging Cognitive Science. Mahwah, NJ: Erlbaum.
  2. Schunn, C. D., Lovett, M. C., Munro, P., & Lebiere, C. (Eds.) (2004). Proceedings of the 2004 Sixth International Conference on Cognitive Modeling. Mahwah, NJ: Erlbaum.
  3. Gray, W. D., & Schunn, C. D. (Eds.) (2002). Proceedings of the 24th Annual Meeting of the Cognitive Science Society. Mahwah, NJ: Erlbaum. pdf
  4. Altmann, E. M., Cleeremans, A., Schunn, C. D., & Gray, W. D. (Eds.) (2001). Proceedings of the 2001 Fourth International Conference on Cognitive Modeling. Mahwah, NJ: Erlbaum.
  5. Crowley, K., Schunn, C. D., & Okada, T. (Eds.). (2001). Designing for Science: Implications from Professional, Instructional, and Everyday Science. Mawah, NJ: Erlbaum. Table of contents

Sharon J. Derry (Editor),
Christian D. Schunn (Editor),
Morton Ann Gernsbacher (Editor) Interdisciplinary Collaboration:
An Emerging Cognitive Science

Interdisciplinary Collaboration calls attention to a serious need to study the problems and processes of interdisciplinary inquiry, to reflect on the current state of scientific knowledge regarding interdisciplinary collaboration, and to encourage research that studies interdisciplinary cognition in relation to the ecological contexts in which it occurs. It contains reflections and research on interdisciplinarity found in a number of different contexts by practitioners and scientists from a number of disciplines and several chapters represent attempts by cognitive scientists to look critically at the cognitive science enterprise itself.

Kevin Crowley (Editor),
Christian D. Schunn (Editor),
Takeshi Okada (Editor) Designing for Science

This volume explores the integration of recent research on everyday, classroom, and professional scientific thinking. It brings together an international group of researchers to present core findings from each context; discuss connections between contexts, and explore structures; technologies, and environments to facilitate the development and practice of scientific thinking.