Science Reasoning & Engineering Design
Egan, P., Chiu, F., Cagan, J., Schunn, C., LeDuc, P., Moore, J. (2016). The d3 design methodology: Bridging science and design for bio-based product development. Journal of Mechanical Design, 138, 081101-1-13. pdf
Paletz, S., Chan, J., & Schunn, C. D. (2016). Uncovering uncertainty through disagreement. Applied Cognitive Psychology, 30(3), 387-400. pdf
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
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
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
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
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
Chan, J., & Schunn, C. D. (2015).The impact of analogies on creative concept generation: Lessons from an in vivo study in engineering design. Cognitive Sciencee, 39(1), 126-155. pdf
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Paletz,
S. B. F., & Schunn, C. D. (2010). A social-cognitive
framework of multidisciplinary team innovation. Topics in Cognitive
Science, 2, 73-95. pdf
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
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
Christensen,
B. T., & Schunn, C. D. (2009). The
role and impact of mental simulation in design. Applied Cognitive Psychology,
23, 327-344. pdf
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
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
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
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
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
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
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
Christensen,
B. T., & Schunn, C. D. (2005). Spontaneous access and
analogical incubation effects. Creativity Research Journal,
17(2), 207-220. pdf
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
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)
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
Schunn,
C. D., & Anderson, J. R. (1999). The generality/specificity
of expertise in scientific reasoning. Cognitive Science, 23(3),
337-370. pdf
Schunn,
C. D., & Anderson, J. R. (1998). Scientific Discovery. In
J. R. Anderson & C. Lebiere (Eds.), Atomic Components of Thought. Mahwah,
NJ: Erlbaum. pdf
Schunn,
C. D., Crowley, K., & Okada, T. (1998). The growth of multidisciplinarity
in the Cognitive Science Society. Cognitive Science, 22(1),
107-130.pdf
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
Cromley, J. M., Weisberg, S. M., Dai, T., Newcombe, N. S., Schunn, C. D., Massey, C., Merlino, F. J. (in press). Improving middle school science learning using diagrammatic reasoning. Science Education.
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
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
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
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
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
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
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
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
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
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
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
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.
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
Apedoe, X. & Schunn, C. D. (2013). Strategies for Success: Uncovering what makes students successful in design and learning. Instructional science, 41, 773-791. pdf
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
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
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
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
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
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
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
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
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
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
Schunn,
C. D. (2009). How Kids Learn Engineering: The Cognitive
Science Perspective. The Bridge, 39(3), 32-37. pdf (in Hebrew)
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
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
Schunn, C. D. (2008). Engineering educational
design. Educational Designer, 1. html (in Hebrew)
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
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
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
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
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
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
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
Patchan, M. M., Schunn, C. D., & Correnti, R. (In press). The nature of feedback: how feedback features affect students' implementation rate and quality of revisions. Journal of Educational Psychology. pdf
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
Patchan, M. M., & Schunn, C. D. (2016). Understanding the benefits of receiving peer feedback: A case of matching ability in peer-review. Journal of Writing Research, 8(1). pdf
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
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
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
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
Xiong, W., Litman, D., & Schunn, C. D. (2012). Redesigning peer review interactions using computer tools. Journal of Writing Research, 4(2), 155-176. pdf
Lee,
C. J., & Schunn,
C.D. (2011). Social biases and solutions for procedural objectivity.
Hypatia, 26(2), 352-373. pdf
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
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
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
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
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
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
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
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
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
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
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
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
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
Kallai, A. Y., Schunn, C. D., & Fiez, J. A. (2012). Mental arithmetic activates analogic representations of internally generated sums. Neuropsychologia, 50, 2397–2407. pdf
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
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 and Learning
Bathgate, M. E., & Schunn, C. D. (In press). Disentangling intensity from breadth of science interest: What predicts learning behaviors? Instructional Science.
Akiva, T., Kehoe, S., & Schunn, C. D. (In press). 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.
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
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
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
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
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.
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
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
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
Kirschenbaum, S. S., Schunn, C. D., & Trafton, J. G. (2014). Visualizing uncertainty: The impact on performance. Human Factors, 56(3), 509-520. pdf
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
Jang,
J., & Schunn, C. D. (2012). Performance benefits of
spatially distributed vs. stacked information on integration tasks.
Applied Cognitive Psychology, 26, 207-214. pdf
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
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
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
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
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
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
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
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
Schunn,
C. D., Lovett, M., & Reder, L. M. (2001). Awareness and working
memory in strategy adaptivity. Memory & Cognition, 29(2),
256-266. pdf
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
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
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
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
Schunn,
C. D., & Reder, L. M. (1998). Individual differences in strategy
adaptivity. In D. L. Medin (Ed.), The psychology of learning and
motivation. pdf
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
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
Liu, A. S., & Schunn, C. D. (2016). The central questions of spatial cognition. To appear in S. Chipman (Ed.), The Oxford Handbook of Cognitive Science. Oxford University Press. pdf
Alfieri, L., Nokes, T. N., & Schunn, C. D. (2013). Learning through case comparisons: a meta-analytic review. Educational Psychologist, 48(2), 87-113. pdf
Altmann, E. M. & Schunn, C. D. (2012). Decay versus Interference: A new look at an old interaction. Psychological Science, 23(11), 1435-1437. pdf
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
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
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
Schunn,
C. D., (2009). John Robert Anderson biography. In Eric Anderman (Ed.), Psychology of Classroom Learning: An Encyclopedia.
Detroit, MI: Macmillan Reference.
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
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
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
Schunn,
C. D., & Vera, A. H. (2004). Cross-cultural similarities
in category structure. Thinking & Reasoning, 10(3),
273-287. pdf
Schunn,
C. D., & Gray, W. D. (2002). Introduction to the special
issue on computational cognitive modeling. Cognitive Systems Research, 3, 1-3. pdf
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
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
Schunn,
C. D., & Klahr, D. (1998). Stances: Production systems. In
W. Bechtel and G. Graham (Eds.), A Companion to Cognitive Science. Blackwell. pdf
Schunn,
C. D., & Vera, A. H. (1995). Causality and the categorization
of objects and events. Thinking & Reasoning, 1(3), 237-284. Full
paper
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