Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T19:17:07.707Z Has data issue: false hasContentIssue false

Part I - Foundations

Published online by Cambridge University Press:  14 March 2022

R. Keith Sawyer
Affiliation:
University of North Carolina, Chapel Hill
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Abrahamson, D., & Sánchez-García, R. (2016). Learning is moving in new ways: The ecological dynamics of mathematics education. Journal of the Learning Sciences, 25(2), 203239.Google Scholar
Alibali, M. W., & Nathan, M. J. (2012). Embodiment in mathematics teaching and learning: Evidence from learners’ and teachers’ gestures. Journal of the Learning Sciences, 21(2), 247286.CrossRefGoogle Scholar
Anderson, J. R. (2005). Cognitive psychology and its implications. New York, NY: Macmillan.Google Scholar
Anderson, J. R., Greeno, J. G., Reder, L. M., & Simon, H. A. (2000). Perspectives on learning, thinking, and activity. Educational Researcher, 29(4), 1113.CrossRefGoogle Scholar
Azmitia, M. (1996). Peer interactive minds: Developmental, theoretical, and methodological issues. In Baltes, P. B. & Staudinger, U. M. (Eds.), Interactive minds: Life-span perspectives on the social foundation of cognition (pp. 133162). New York, NY: Cambridge University Press.Google Scholar
Barab, S., Thomas, M., Dodge, T., Carteaux, R., & Tuzun, H. (2005). Making learning fun: Quest Atlantis, a game without guns. Educational Technology Research and Development, 53(1), 86107.CrossRefGoogle Scholar
Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307359.Google Scholar
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617645.Google Scholar
Biswas, G., Leelawong, K., Schwartz, D., Vye, N., & The Teachable Agents Group at Vanderbilt. (2005). Learning by teaching: A new agent paradigm for educational software. Applied Artificial Intelligence, 19(3–4), 363392.CrossRefGoogle Scholar
Brown, A. L., & Campione, J. C. (1994). Guided discovery in a community of learners. In McGilly, K. (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 229270). Cambridge, MA: MIT Press/Bradford Books.Google Scholar
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 3242.CrossRefGoogle Scholar
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219243.CrossRefGoogle Scholar
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 719.Google Scholar
Cognition and Technology Group at Vanderbilt. (1997). The Jasper Project: Lessons in curriculum, instruction, assessment, and professional development. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64(1), 135.CrossRefGoogle Scholar
Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge, MA: Harvard University Press.Google Scholar
Davenport, J. L., Kao, Y. S., Matlen, B. J., & Schneider, S. A. (2020). Cognition research in practice: Engineering and evaluating a middle school math curriculum. The Journal of Experimental Education, 88(4), 516535.Google Scholar
Dede, C. (2006). Evolving innovations beyond ideal settings to challenging contexts of practice. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 551566). New York, NY: Cambridge University Press.Google Scholar
Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 58.Google Scholar
Dillenbourg, P. (1999). What do you mean by collaborative learning?. In Dillenbourg, P. (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 119). Oxford, England: Elsevier.Google Scholar
Dillenbourg, P., & Self, J. (1992). People power: A human-computer collaborative learning system. In Frasson, C., Gauthier, G., & McCalla, G. (Eds.), The 2nd International Conference of Intelligent Tutoring Systems (Lecture Notes in Computer Science, 608, pp. 651660). London, England: Springer-Verlag.Google Scholar
Dreyfus, H. L. (2002). Intelligence without representation – Merleau-Ponty’s critique of mental representation: The relevance of phenomenology to scientific explanation. Phenomenology and the Cognitive Sciences, 1(4), 367383.Google Scholar
Dunlosky, J., & Rawson, K. A. (Eds.). (2019). The Cambridge handbook of cognition and education. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 458.Google Scholar
Edelson, D. C., & Reiser, B. J. (2006). Making authentic practices accessible to learners: Design challenges and strategies. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 335354). New York, NY: Cambridge University Press.Google Scholar
Enyedy, N., Danish, J. A., Delacruz, G., & Kumar, M. (2012). Learning physics through play in an augmented reality environment. International Journal of Computer-Supported Collaborative Learning, 7(3), 347378.CrossRefGoogle Scholar
Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000). Executive attention and metacognitive regulation. Consciousness and Cognition, 9(2), 288307.CrossRefGoogle ScholarPubMed
Flavell, J. H. (1963). The developmental psychology of Jean Piaget (Vol. 1). Princeton, NJ: Van Nostrand.Google Scholar
Furtak, E. M., Seidel, T., Iverson, H., & Briggs, D. C. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: A meta-analysis. Review of Educational Research, 82(3), 300329.Google Scholar
Garfinkel, H. (1967). Studies in ethnomethodology. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Gibson, J. J. (1977). The concept of affordances. In Shaw, R. and Bransford, J. (Eds.), Perceiving, acting, and knowing: Toward an ecological psychology (pp. 6782). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Glenberg, A. M. (1997). What memory is for: Creating meaning in the service of action. Behavioral and Brain Sciences, 20(1), 4150.Google Scholar
Glenberg, A. M., Gutiérrez, T., Levin, J. R., Japuntich, S., & Kaschak, M. P. (2004). Activity and imagined activity can enhance young children’s reading comprehension. Journal of Educational Psychology, 96(3), 424436.Google Scholar
Greeno, J. G. (1997). On claims that answer the wrong questions. Educational Researcher, 26(1), 517.Google Scholar
Heath, C., & Luff, P. (1991). Collaborative activity and technological design: Task coordination in the London Underground control rooms. Paper presented at the Proceedings of ECSCW’91.CrossRefGoogle Scholar
Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99107.CrossRefGoogle Scholar
Hughes, J. A., Shapiro, D. Z., Sharrock, W. W., Anderson, R. J., & Gibbons, S. C. (1988). The automation of air traffic control (Final Report SERC/ESRC Grant no. GR/D/86257). Lancaster, England: Department of Sociology, Lancaster University.Google Scholar
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
Jacobson, M. J., Levin, J. A., & Kapur, M. (2019). Education as a complex system: Conceptual and methodological implications. Educational Researcher, 48(2), 112119.Google Scholar
Jessen, F., Heun, R., Erb, M., et al. (2000). The concreteness effect: Evidence for dual coding and context availability. Brain and Language, 74(1), 103112.Google Scholar
Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4(1), 39103.Google Scholar
Klahr, D. (2019). Learning sciences research and Pasteur’s quadrant. Journal of the Learning Sciences, 28(2), 153159.Google Scholar
Koedinger, K. R., Aleven, V., Roll, I., & Baker, R. (2009). In vivo experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. In Hacker, D. J., Dunlosky, J., & Graesser, A. C. (Eds.), Handbook of metacognition in education (pp. 383412). New York, NY: Routledge.Google Scholar
Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 6177). New York, NY: Cambridge University Press.Google Scholar
Kolodner, J. L. (1991). The Journal of the Learning Sciences: Effecting changes in education. Journal of the Learning Sciences, 1(1), 16.Google Scholar
Krajcik, J., Czerniak, C., & Berger, C. (2002). Teaching science in elementary and middle school classrooms: A project-based approach (2nd ed.). Boston, MA: McGraw-Hill.Google Scholar
Lamon, M., Secules, T., Petrosino, A. J., Hackett, R., Bransford, J. D., & Goldman, S. R. (1996). Schools for Thought: Overview of the project and lessons learned from one of the sites. In Schauble, L. and Glaser, R. (Eds.), Innovation in learning: New environments for education (pp. 243288). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Larkin, M., Eatough, V., & Osborn, M. (2011). Interpretative phenomenological analysis and embodied, active, situated cognition. Theory & Psychology, 21(3), 318337.Google Scholar
Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life. New York, NY: Cambridge University Press.Google Scholar
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Lehrer, R., Kim, M. J., Ayers, E., & Wilson, M. (2014). Toward establishing a learning progression to support the development of statistical reasoning. In Mahoney, A. P., Confrey, J., & Nyugen, K. H. (Eds.), Learning over time: Learning trajectories in mathematics education (pp. 3160). Charlotte, NC: Information Age Publishers.Google Scholar
Lemke, J. L. (2000). Across the scales of time: Artifacts, activities, and meanings in ecosocial systems. Mind, Culture, and Activity, 7(4), 273290.Google Scholar
Lindgren, R., & Johnson-Glenberg, M. (2013). Emboldened by embodiment: Six precepts for research on embodied learning and mixed reality. Educational Researcher, 42(8), 445452.CrossRefGoogle Scholar
Linn, M. C., & Slotta, J. D. (2000). WISE science. Educational Leadership, 58(2), 2932.Google Scholar
Marx, R. W., Blumenfeld, P. C., Krajcik, J. S., et al. (2004). Inquiry-based science in the middle grades: Assessment of learning in urban systemic reform. Journal of Research in Science Teaching, 41(10), 10631080.CrossRefGoogle Scholar
McNamara, D. S., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14(1), 143.CrossRefGoogle Scholar
Nathan, M. J. (2012). Rethinking formalisms in formal education. Educational Psychologist, 47(2), 125148.CrossRefGoogle Scholar
Nathan, M. J. (2014). Grounded mathematical reasoning. In Shapiro, L. (Ed.), The Routledge handbook of embodied cognition (pp. 171183). New York, NY: Routledge.Google Scholar
Nathan, M. J. (2021). Foundations of embodied learning: A paradigm for education. New York, NY: Routledge.Google Scholar
Nathan, M. J., & Alibali, M. W. (2010). Learning sciences. Wiley Interdisciplinary Reviews: Cognitive Science, 1(3), 329345.Google Scholar
Nathan, M. J., & Swart, M. I. (2021). Materialist epistemology lends design wings: Educational design as an embodied process. Educational Technology Research and Development, 69(4), 19251954.CrossRefGoogle Scholar
Nathan, M. J., & Walkington, C. (2017). Grounded and embodied mathematical cognition: Promoting mathematical insight and proof using action and language. Cognitive Research: Principles and Implications, 2(1), 9.Google ScholarPubMed
Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
Ochs, E., Jacoby, S., & Gonzales, P. (1994). Interpretive journeys: How physicists talk and travel through graphic space. Configurations, 2(1), 151171.Google Scholar
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books.Google Scholar
Pashler, H., Bain, P. M., Bottge, B. A., et al. (2007). Organizing instruction and study to improve student learning [IES Practice Guide; NCER 2007–2004]. National Center for Education Research.CrossRefGoogle Scholar
Penuel, W. R., Fishman, B. J., Cheng, B. H., & Sabelli, N. (2011). Organizing research and development at the intersection of learning, implementation, and design. Educational Researcher, 40(7), 331337.Google Scholar
Perfetti, C. A. (1989). There are generalized abilities and one of them is reading. In Resnick, L. (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 307335). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Resnick, M., Maloney, J., Monroy-Hernández, A., et al. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 6067.Google Scholar
Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. Oxford, England: Oxford University Press.Google Scholar
Rummel, N., Spada, H., & Hauser, S. (2009). Learning to collaborate while being scripted or by observing a model. International Journal of Computer-Supported Collaborative Learning, 4(1), 6992.CrossRefGoogle Scholar
Salomon, G. (Ed.). (1993). Distributed cognitions: Psychological and educational considerations. Cambridge, England: Cambridge University Press.Google Scholar
Sawyer, R. K. (2005). Social emergence: Societies as complex systems. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Saxe, G. B. (1991). Culture and cognitive development: Studies in mathematical understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Sfard, A. (1998). On two metaphors for learning and the dangers of choosing just one. Educational Researcher, 27(2), 413.CrossRefGoogle Scholar
Shaffer, D. W. (2017). Quantitative ethnography. Lulu.com.Google Scholar
Simon, H. A. (1996). The sciences of the artificial. Cambridge, MA: MIT Press.Google Scholar
Songer, N. B. (1996). Exploring learning opportunities in coordinated network-enhanced classrooms: A case of kids as global scientists. Journal of the Learning Sciences, 5(4), 297327.Google Scholar
Spillane, J. P., Reiser, B. J., & Reimer, T. (2002). Policy implementation and cognition: Reframing and refocusing implementation research. Review of Educational Research, 72(3), 387431.Google Scholar
Stokes, D. E. (1997). Pasteur's quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Google Scholar
Strauss, A., & Corbin, J. M. (1997). Grounded theory in practice. London, England: Sage Publications.Google Scholar
Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. New York, NY: Cambridge University Press.Google Scholar
Tatar, D., Roschelle, J., Knudsen, J., Shechtman, N., Kaput, J., & Hopkins, B. (2008). Scaling up innovative technology-based mathematics. Journal of the Learning Sciences, 17(2), 248286.Google Scholar
Ur, S., & VanLehn, K. (1995). Steps: A simulated, tutorable physics student!. Journal of Artificial Intelligence in Education, 6(4), 405437.Google Scholar
Van den Broek, G., Takashima, A., Wiklund-Hörnqvist, C., et al. (2016). Neurocognitive mechanisms of the “testing effect”: A review. Trends in Neuroscience and Education, 5(2), 5266.Google Scholar
Von Glasersfeld, E. (1989). Cognition, construction of knowledge, and teaching. Synthese, 80(1), 121140.Google Scholar
Vygotsky, L. S. (1978). Mind in society: The development of higher mental process. Cambridge, MA: Harvard University Press.Google Scholar
Yoon, S. A., & Hmelo-Silver, C. E. (2017). What do learning scientists do? A survey of the ISLS membership. Journal of the Learning Sciences, 26(2), 167183.Google Scholar

References

Azevedo, R., Moos, D. C., Greene, J. A., Winters, F. I., & Cromley, J. G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development, 56(1), 4572. doi:10.1007/s11423-007-9067-0Google Scholar
Bang, M. (2017). Towards an ethic of decolonial trans-ontologies in sociocultural theories of learning and development. In Esmonde, I. & Booker, A. N. (Eds.), Power and privilege in the learning sciences: Critical and sociocultural theories of learning (pp. 115138). New York, NY: Routledge.Google Scholar
Barron, B., Schwartz, D. L., Vye, N. J., et al. (1998). Doing with understanding: Lessons from research on problem- and project-based learning. Journal of the Learning Sciences, 7(3–4), 271311.Google Scholar
Basu, S., Biswas, G., & Kinnebrew, J. S. (2017). Learner modeling for adaptive scaffolding in a computational thinking-based science learning environment. User Modeling and User-Adapted Interaction, 27(1), 553. doi:10.1007/s11257-017-9187-0Google Scholar
Bell, P., Van Horne, K., & Cheng, B. H. (2017). Special issue: Designing learning environments for equitable disciplinary identification. Journal of the Learning Sciences, 26(3), 367375. doi:10.1080/10508406.2017.1336021CrossRefGoogle Scholar
Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2016). Synthesizing results from empirical research on computer-based scaffolding in stem education: A meta-analysis. Review of Educational Research, 87(2), 309344. doi:10.3102/0034654316670999Google Scholar
Berland, L. K. (2011). Explaining variation in how classroom communities adapt the practice of scientific argumentation. Journal of the Learning Sciences, 20(4), 625664.Google Scholar
Berland, L. K., & Hammer, D. (2012). Students’ framings and their participation in scientific argumentation. In Khine, M. S. (Ed.), Perspectives on scientific argumentation: Theory, practice and research (pp. 7393). New York, NY: Springer.Google Scholar
Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S., & Reiser, B. J. (2016). Epistemologies in practice: Making scientific practices meaningful for students. Journal of Research in Science Teaching, 53(7), 10821112. doi:10.1002/tea.21257Google Scholar
Blumenfeld, P., Soloway, E., Marx, R., Krajcik, J., Guzdial, M., & Palincsar, A. S. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26(3–4), 369398.Google Scholar
Bransford, J. D., Brown, A., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience and schools. Washington, DC: National Academy Press.Google Scholar
Bulu, S. T., & Pedersen, S. (2010). Scaffolding middle school students’ content knowledge and ill-structured problem solving in a problem-based hypermedia learning environment. Educational Technology Research and Development, 58(5), 507529. doi:10.1007/s11423-010-9150-9Google Scholar
Cazden, C. B. (1979). Peekaboo as an instructional model: Discourse development at home and at school. Papers and Reports on Child Language Development, No. 17. Department of Linguistics, Stanford University, CA.Google Scholar
Cazden, C. B. (1997). Performance before competence: Assistance to child discourse in the zone of proximal development. In Cole, M., Engestrom, Y., & Vasquez, O. (Eds.), Mind, culture, and activity: Seminal papers from the laboratory of comparative human cognition (pp. 303310). New York, NY: Cambridge University Press.Google Scholar
Chang, H.-Y., & Linn, M. C. (2013). Scaffolding learning from molecular visualizations. Journal of Research in Science Teaching, 50(7), 858886. doi:10.1002/tea.21089Google Scholar
Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In Resnick, L. B. (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453494). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Davis, E. A. (2003). Prompting middle school science students for productive reflection: Generic and directed prompts. Journal of the Learning Sciences, 12(1), 91142.Google Scholar
de Jong, T. (2006). Scaffolds for scientific discovery learning. In Elen, J. & Clark, R. E. (Eds.), Handling complexity in learning environments: Theory and research (pp. 107128). Amsterdam, The Netherlands: Elsevier.Google Scholar
de Vries, E., Lund, K., & Baker, M. (2002). Computer-mediated epistemic dialogue: Explanation and argumentation as vehicles for understanding scientific notions. Journal of the Learning Sciences, 11(1), 63103.Google Scholar
Demetriadis, S. N., Papadopoulos, P. M., Stamelos, I. G., & Fischer, F. (2008). The effect of scaffolding students’ context-generating cognitive activity in technology-enhanced case-based learning. Computers & Education, 51(2), 939954.Google Scholar
Díaz, A., Nussbaum, M., Ñopo, H., Maldonado-Carreño, C., & Corredor, J. (2015). Orchestration: Providing teachers with scaffolding to address curriculum standards and students’ pace of learning. Journal of Educational Technology & Society, 18(3), 226239. doi:10.2307/jeductechsoci.18.3.226Google Scholar
Edelson, D. C. (2001). Learning-for-use: A framework for integrating content and process learning in the design of inquiry activities. Journal of Research in Science Teaching, 38(3), 355385.Google Scholar
Edelson, D. C., & Reiser, B. J. (2006). Making authentic practices accessible to learners: Design challenges and strategies. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 335354). New York, NY: Cambridge University Press.Google Scholar
Engle, R. A., & Conant, F. R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20(4), 399483.Google Scholar
Esmonde, I., & Booker, A. N. (Eds.). (2017). Power and privilege in the learning sciences: Critical and sociocultural theories of learning. New York, NY: Routledge.Google Scholar
Fretz, E. B., Wu, H.-K., Zhang, B., Davis, E. A., Krajcik, J. S., & Soloway, E. (2002). An investigation of software scaffolds supporting modeling practices. Research in Science Education, 32(4), 567589. doi:10.1023/a:1022400817926Google Scholar
Gagné, R. M. (1965). The conditions of learning. New York, NY: Holt, Rinehart, and Winston.Google Scholar
Gerard, L., Matuk, C., McElhaney, K., & Linn, M. C. (2015). Automated, adaptive guidance for K-12 education. Educational Research Review, 15(1), 4158. doi:10.1016/j.edurev.2015.04.001Google Scholar
Gidalevich, S., & Kramarski, B. (2019). The value of fixed versus faded self-regulatory scaffolds on fourth graders’ mathematical problem solving. Instructional Science, 47(1), 3968. doi:10.1007/s11251-018-9475-zGoogle Scholar
Greenfield, P. M. (1984). A theory of teacher in the learning activities of everyday life. In Rogoff, B. & Lave, J. (Eds.), Everyday cognition: Its development in social context (pp. 117138). Cambridge, MA: Harvard University Press.Google Scholar
Greeno, J. G., Collins, A. M., & Resnick, L. B. (1996). Cognition and learning. In Berliner, D. C. & Calfee, R. C. (Eds.), Handbook of educational psychology (pp. 1546). New York, NY; London, England: Macmillan Library Reference USA; Prentice Hall International.Google Scholar
Gutiérrez, K., & Stone, L. (2002). Hypermediating literacy activity: How learning contexts get reorganized. In Saracho, O. & Spodek, B. (Eds.), Contemporary perspectives in literacy in early childhood education (pp. 2551). Greenwich, CT: Information Age Publishing.Google Scholar
Guzdial, M. (1994). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 144.CrossRefGoogle Scholar
Hermkes, R., Mach, H., & Minnameier, G. (2018). Interaction-based coding of scaffolding processes. Learning and Instruction, 54(1), 147155. doi:10.1016/j.learninstruc.2017.09.003Google Scholar
Herrenkohl, L. R., & Bevan, B. (2017). What science and for whom? An introduction to our focus on equity and out-of-school learning. Science Education, 101(4), 517519. doi:10.1002/sce.21284Google Scholar
Herrenkohl, L. R., Palincsar, A. S., DeWater, L. S., & Kawasaki, K. (1999). Developing scientific communities in classrooms: A sociocognitive approach. Journal of the Learning Sciences, 8(3–4), 451493.Google Scholar
Herrenkohl, L. R., Tasker, T., & White, B. (2011). Pedagogical practices to support classroom cultures of scientific inquiry. Cognition and Instruction, 29(1), 144.Google Scholar
Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learn about complex systems. Journal of the Learning Sciences, 9(3), 247298.CrossRefGoogle Scholar
Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99107.Google Scholar
Hutchins, E. (1996). Learning to navigate. In Chaiklin, S. & Lave, J. (Eds.), Understanding practice: Perspectives on activity and context (pp. 3563). New York, NY: Cambridge University Press.Google Scholar
Jordan, B. (1989). Cosmopolitical obstetrics: Some insights from the training of traditional midwives. Social Science & Medicine, 28(9), 925937.Google Scholar
Kali, Y., Linn, M. C., & Roseman, J. E. (2008). Designing coherent science education: Implications for curriculum, instruction, and policy. New York, NY: Teachers College Press.Google Scholar
Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. Journal of the Learning Sciences, 21(1), 4583. doi:10.1080/10508406.2011.591717Google Scholar
Koedinger, K., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning sciences to the classroom. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 6196). West Nyack, NY: Cambridge University Press.Google Scholar
Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts – A conceptual analysis. Educational Psychology Review, 18(2), 159185.Google Scholar
Kyza, E. A. (2009). Middle-school students’ reasoning about alternative hypotheses in a scaffolded, software-based inquiry investigation. Cognition and Instruction, 27(4), 277311. doi:10.1080/07370000903221718Google Scholar
Lajoie, S. P. (2005). Extending the scaffolding metaphor. Instructional Science, 33(5–6), 541557.Google Scholar
Lave, J. (1997). The culture of acquisition and the practice of understanding. In Kirshner, D. & Whitson, J. A. (Eds.), Situated cognition: Social, semiotic, and psychological perspectives (pp. 6382). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Lee, C. D. (2001). Is October Brown Chinese? A cultural modeling activity system for underachieving students. American Educational Research Journal, 38(1), 97141.Google Scholar
Lee, C. D. (2017). Expanding visions of how people learn: The centrality of identity repertoires. Journal of the Learning Sciences, 26(3), 517524. doi:10.1080/10508406.2017.1336022Google Scholar
Lee, C.-Y., & Chen, M.-P. (2009). A computer game as a context for non-routine mathematical problem solving: The effects of type of question prompt and level of prior knowledge. Computers & Education, 52(3), 530542.Google Scholar
Lefstein, A., Vedder-Weiss, D., Tabak, I., & Segal, A. (2018). Learner agency in scaffolding: The case of coaching teacher leadership. International Journal of Educational Research, 90, 209222. doi:10.1016/j.ijer.2017.11.002Google Scholar
Lehrer, R., & Schauble, L. (2006). Scientific thinking and science literacy: Supporting development in learning in contexts. In Damon, W., Lerner, R. M., Renninger, K. A., & Sigel, I. E. (Eds.), Handbook of child psychology (6th ed., Vol. 4, pp. 153196). Hoboken, NJ: John Wiley & Sons.Google Scholar
Lepper, M. R., Woolverton, M., Mumme, D. L., & Gurtner, J. (1993). Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In Lajoie, S. P. & Derry, S. J. (Eds.), Computers as cognitive tools (pp. 75105). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Linn, M. C., Bell, P., & Davis, E. A. (2004). Specific design principles: Elaborating the scaffolded knowledge integration framework. In Linn, M. C., Davis, E. A., & Bell, P. (Eds.), Internet environments for science education (pp. 315340). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Linn, M. C., Clark, D., & Slotta, J. D. (2003). Wise design for knowledge integration. Science Education, 87(4), 517538.Google Scholar
Martin, N. D., Dornfeld Tissenbaum, C., Gnesdilow, D., & Puntambekar, S. (2019). Fading distributed scaffolds: The importance of complementarity between teacher and material scaffolds. Instructional Science, 47(1), 6998. doi:10.1007/s11251-018-9474-0Google Scholar
McClain, K., & Cobb, P. (1997). An analysis of the teacher’s role in guiding the evolution of sociomathematical norms. Paper presented at the 1997 Annual Meeting of the American Educational Research Association, Chicago, IL.Google Scholar
McClain, K., & Cobb, P. (2001). An analysis of development of sociomathematical norms in one first-grade classroom. Journal for Research in Mathematics Education, 32(3), 236266.Google Scholar
McNeill, K. L., & Krajcik, J. (2009). Synergy between teacher practices and curricular scaffolds to support students in using domain-specific and domain-general knowledge in writing arguments to explain phenomena. Journal of the Learning Sciences, 18(3), 416460. doi:10.1080/10508400903013488Google Scholar
Merrill, D. C., Reiser, B. J., Merrill, S. K., & Landes, S. (1995). Tutoring: Guided learning by doing. Cognition and Instruction, 13(3), 315372.Google Scholar
Metz, K. E. (2011). Disentangling robust developmental constraints from the instructionally mutable: Young children’s epistemic reasoning about a study of their own design. Journal of the Learning Sciences, 20(1), 50110.Google Scholar
Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in Philosophy and Education, 27(4), 283297.Google Scholar
Nussbaum, E. M., & Edwards, O. V. (2011). Critical questions and argument stratagems: A framework for enhancing and analyzing students’ reasoning practices. Journal of the Learning Sciences, 20(3), 443488.Google Scholar
O’Connor, M. C., & Michaels, S. (1993). Aligning academic task and participation status through revoicing: Analysis of a classroom discourse strategy. Anthropology and Education Quarterly, 24(4), 318335.Google Scholar
Palincsar, A. S. (1998). Keeping the metaphor of scaffolding fresh – A response to C. Addison Stone’s “The metaphor of scaffolding: Its utility for the field of learning disabilities.” Journal of Learning Disabilities, 31(4), 370373.Google Scholar
Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117175.Google Scholar
Palincsar, A. S., Fitzgerald, M. S., Marcum, M. B., & Sherwood, C.-A. (2018). Examining the work of “scaffolding” in theory and practice: A case study of 6th graders and their teacher interacting with one another, an ambitious science curriculum, and mobile devices. International Journal of Educational Research, 90(1), 191208. doi:10.1016/j.ijer.2017.11.006Google Scholar
Pea, R. D. (2004). The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity. Journal of the Learning Sciences, 13(3), 423451. doi:10.1207/s15327809jls1303_6Google Scholar
Puntambekar, S., & Hubscher, R. (2005). Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist, 40(1), 112. doi:10.1207/s15326985ep4001_1Google Scholar
Puntambekar, S., & Kolodner, J. L. (2005). Distributed scaffolding: Helping students learn science from design. Journal of Research in Science Teaching, 42(2), 185217.Google Scholar
Quintana, C., Reiser, B. J., Davis, E. A., et al. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13(3), 337386.Google Scholar
Radinsky, J., & Tabak, I. (2017). Outgoing editors’ note: The Journal of the Learning Sciences as a mirror of trends in the field. Journal of the Learning Sciences, 26(1), 16. doi:10.1080/10508406.2017.1260414Google Scholar
Raes, A., Schellens, T., De Wever, B., & Vanderhoven, E. (2012). Scaffolding information problem solving in web-based collaborative inquiry learning. Computers & Education, 59(1), 8294.Google Scholar
Ratner, N., & Bruner, J. (1978). Games, social exchange and the acquisition of language. Journal of Child Language, 5(3), 391401.Google Scholar
Reid, D. K., & Stone, C. A. (1991). Why is cognitive instruction effective? Underlying learning mechanisms. Remedial and Special Education, 12(3), 819.Google Scholar
Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273304.Google Scholar
Reiser, B. J., Michaels, S., Moon, J., et al. (2017). Scaling up three-dimensional science learning through teacher-led study groups across a state. Journal of Teacher Education, 68(3), 280298. doi:10.1177/0022487117699598Google Scholar
Reisman, A. (2012). Reading like a historian: A document-based history curriculum intervention in urban high schools. Cognition and Instruction, 30(1), 86112. doi:10.1080/07370008.2011.634081Google Scholar
Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. New York, NY: Oxford University Press.Google Scholar
Sawyer, R. K. (2019). The creative classroom: Innovative teaching for 21st-century learners. New York, NY: Teachers College Press.Google Scholar
Schwarz, C. V., Reiser, B. J., Davis, E. A., et al. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632654.Google Scholar
Seixas, P. (2017). A model of historical thinking. Educational Philosophy and Theory, 49(6), 593605. doi:10.1080/00131857.2015.1101363Google Scholar
Sherin, B. L., Reiser, B. J., & Edelson, D. C. (2004). Scaffolding analysis: Extending the scaffolding metaphor to learning artifacts. Journal of the Learning Sciences, 13(3), 387421.Google Scholar
Smith, C., Maclin, D., Houghton, C., & Hennessey, M. G. (2000). Sixth-grade students’ epistemologies of science: The impact of school science experiences on epistemological development. Cognition & Instruction, 18(3), 349422.Google Scholar
Stone, C. A. (1998). The metaphor of scaffolding: Its utility for the field of learning disabilities. Journal of Learning Disabilities, 31(4), 344364.Google Scholar
Suthers, D. D., Vatrapu, R., Medina, R., Joseph, S., & Dwyer, N. (2008). Beyond threaded discussion: Representational guidance in asynchronous collaborative learning environments. Computers and Education, 50(4), 11031127.Google Scholar
Tabak, I. (2004). Synergy: A complement to emerging patterns of distributed scaffolding. Journal of the Learning Sciences, 13(3), 305335.CrossRefGoogle Scholar
Tabak, I., & Baumgartner, E. (2004). The teacher as partner: Exploring participant structures, symmetry and identity work in scaffolding. Cognition and Instruction, 22(4), 393429.Google Scholar
Tabak, I., & Kyza, E. A. (2018). Research on scaffolding in the learning sciences: A methodological perspective. In Fischer, F., Hmelo-Silver, C. E., Goldman, S. R., & Reimann, P. (Eds.), International handbook of the learning sciences (pp. 191200). New York, NY: Routledge.CrossRefGoogle Scholar
Tabak, I., & Reiser, B. J. (2008). Software-realized inquiry support for cultivating a disciplinary stance. Pragmatics & Cognition, 16(2), 307355.Google Scholar
Tawfik, A. A., Law, V., Ge, X., Xing, W., & Kim, K. (2018). The effect of sustained vs. faded scaffolding on students’ argumentation in ill-structured problem solving. Computers in Human Behavior, 87, 436449. doi:10.1016/j.chb.2018.01.035Google Scholar
van de Pol, J., Mercer, N., & Volman, M. (2019). Scaffolding student understanding in small-group work: Students’ uptake of teacher support in subsequent small-group interaction. Journal of the Learning Sciences, 28(2), 206239. doi:10.1080/10508406.2018.1522258Google Scholar
van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher–student interaction: A decade of research. Educational Psychology Review, 22(3), 271296. doi:10.1007/s10648-010-9127-6Google Scholar
Vattam, S., Goel, A. K., Rugaber, S., et al. (2011). Understanding complex natural systems by articulating structure-behavior-function models. Educational Technology & Society, 14(1), 6681.Google Scholar
Vogel, F., Wecker, C., Kollar, I., & Fischer, F. (2017). Socio-cognitive scaffolding with computer-supported collaboration scripts: A meta-analysis. Educational Psychology Review, 29(3), 477511. doi:10.1007/s10648-016-9361-7Google Scholar
Wertsch, J. V. (1979). From social-interaction to higher psychological processes – Clarification and application of Vygotsky theory. Human Development, 22(1), 122.Google Scholar
Wertsch, J. V., & Stone, C. A. (1985). The concept of internalization in Vygotsky’s account of the genesis of higher mental functions. In Wertsch, J. V. (Ed.), Culture, communication, and cognition: Vygotskian perspectives (pp. 162179). Cambridge, England: Cambridge University Press.Google Scholar
Wineburg, S. (2001). Historical thinking and other unnatural acts. Philadelphia, PA: Temple University Press.Google Scholar
Wong, L.-H., Boticki, I., Sun, J., & Looi, C.-K. (2011). Improving the scaffolds of a mobile-assisted Chinese character forming game via a design-based research cycle. Computers in Human Behavior, 27(5), 17831793.Google Scholar
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89100.Google Scholar
Wu, L., & Looi, C.-K. (2012). Agent prompts: Scaffolding for productive reflection in an intelligent learning environment. Educational Technology & Society, 15(1), 339353.Google Scholar
Yoon, S. A., Anderson, E., Park, M., Elinich, K., & Lin, J. (2018). How augmented reality, textual, and collaborative scaffolds work synergistically to improve learning in a science museum. Research in Science & Technological Education, 36(3), 261281. doi:10.1080/02635143.2017.1386645Google Scholar

References

Baines, A. (2015). Project-based learning increases science achievement in elementary schools and improves social and emotional learning. Lucas Education Research. Retrieved from www.lucasedresearch.org/researchGoogle Scholar
Bielik, T., Stephens, L., Damelin, D., & Krajcik, J. (2019). Designing technology rich environments to support student modeling practice. In Upmeier zu Belzen, A., Kruger, D., & Van Driel, J. (Eds.), Towards a competence-based view on models and modeling in science education (pp. 275290). Cham, Switzerland: Springer International Publishing.Google Scholar
Blumenfeld, P. C., Fishman, B. J., Krajcik, J., Marx, R. W., & Soloway, E. (2000). Creating usable technology – Embedded project-based science in urban schools. Educational Psychologist, 35(3), 149164.Google Scholar
Blumenfeld, P. C, Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26(3–4), 369398.Google Scholar
Bransford, J., Brown, A. L., & Cocking, R. R. (1999). How people learn: Brain, mind experience, and school. Washington, DC: National Academy Press.Google Scholar
Brown, A. L., & Campione, J. C. (1994). Guided discovery in a community of learners. In McGilly, K. (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 229270). Cambridge, MA: MIT Press.Google Scholar
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition of learning. Educational Researcher, 18(1), 3242.Google Scholar
Chiu, M. H., Chou, C. C., Chen, Y. H., et al. (2018). Model-based learning about structures and properties of chemical elements and compounds via the use of augmented realities. Chemistry Teacher International, 1(1), Article 20180002. doi:10.1515/cti-2018-0002Google Scholar
Common Online Data Analysis Platform [computer software]. (2020). Concord, MA: The Concord Consortium.Google Scholar
Condliffe, B., Quint, J., Visher, M. G., et al. (2017). Project based learning: A literature review [Working paper]. New York, NY: MDRC.Google Scholar
Duncan, R., Krajcik, J., & Rivet, A. (Eds.). (2016). Disciplinary core ideas: Reshaping teaching and learning. Arlington, VA: National Science Teachers Association Press.Google Scholar
Edelson, D. C., & Reiser, B. J. (2006). Making authentic practices accessible to learners: Design challenges and strategies. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 335354). New York, NY: Cambridge University Press.Google Scholar
Eidin, E., Bielik, T., Touitou, I., Bowers, J., McIntyre, C., & Damlin, D. (2020). Characterizing advantages and challenges for students engaging in computational thinking and systems thinking through model construction. In Gresalfi, M. & Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020 (Vol. 1, pp. 183190). Nashville, TN: International Society of the Learning Sciences.Google Scholar
Geier, R., Blumenfeld, P., Marx, R., Krajcik, J., Fishman, B., & Soloway, E. (2008). Standardized test outcomes of urban students participating in standards and project based science curricula. Journal of Research in Science Teaching, 45(8), 922939.Google Scholar
Harris, C. J., Krajcik, J., Pellegrino, J., & DeBarger, A. H. (2019). Designing knowledge-in-use assessments to promote deeper learning. Educational Measurement: Issues and Practice, 38(2), 5367.Google Scholar
Harris, C. J., Penuel, W. R., D’Angelo, C. M., et al. (2015). Impact of project-based curriculum materials on student learning in science: Results of a randomized controlled trial. Journal of Research in Science Teaching, 52(10), 13621385. doi:10.1002/tea.21263Google Scholar
Hasni, A., Bousadra, F., Belletête, V., Benabdallah, A., Nicole, M., & Dumais, N. (2016). Trends in research on project-based science and technology teaching and learning at K–12 levels: A systematic review. Studies in Science Education, 52(2), 199231. doi:10.1080/03057267.2016.1226573Google Scholar
Hug, B., & Krajcik, J. (2002). Students, scientific practices using a scaffolded inquiry sequence. In Bell, P., Stevens, R., & Satwicz, T. (Eds.), Keeping learning complex: The proceedings of the Fifth International Conference for the Learning Sciences (ICLS). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Jagers, R. J., Rivas-Drake, D., & Borowski, T. (2018). Equity & social and emotional learning: A cultural analysis. Frameworks Briefs, Special Issues Series.Google Scholar
Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving Schools, 19(3), 267277. doi:10.1177/1365480216659733Google Scholar
Kolodner, J., Krajcik, J., Reiser, B., Edelson, D., & Starr, M. (2013). Project-based inquiry science: It’s about time, publisher [Middle school science curriculum materials]. Mt. Kisco, NY. Retrieved from https://activatelearning.com/pbiscience/Google Scholar
Krajcik, J. S., & Blumenfeld, P. C. (2006). Project-based learning. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 317334). New York, NY: Cambridge.Google Scholar
Krajcik, J. S., Codere, S., Dahsah, C., Bayer, R., & Mun, K. (2014). Planning instruction to meet the intent of the next generation science standards. The Journal of Science Teacher Education, 25(2), 157175. doi:10.1007/s10972–014-9383-2Google Scholar
Krajcik, J. S., & Czerniak, C. M. (2018). Teaching science in elementary and middle school classrooms: A project-based approach (5th ed.). London, England: Taylor & Francis.Google Scholar
Krajcik, J. S., Miller, E., & Chen, I. (in press). Using project-based learning to leverage culturally relevant pedagogy for sensemaking of science in urban elementary classrooms. In Atwater, M. M. (Ed.), The international handbook of research on multicultural science education. Springer.Google Scholar
Krajcik, J. S., & Mun, K. (2014). Promises and challenges of using learning technologies to promote student learning of science. In Lederman, N. G. & Abell, S. K. (Eds.), The handbook of research on science education (pp. 337360). New York, NY: Routledge.Google Scholar
Krajcik, J. S, Schneider, B., Miller, E., et al. (2020). Assessing the effect of project-based learning on science learning in elementary schools: Technical report. San Rafael, CA: George Lucas Educational Foundation.Google Scholar
Krajcik, J. S., & Shin, N. (2014). Project-based learning. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 275297). New York, NY: Cambridge University Press.Google Scholar
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York, NY: Cambridge University Press.Google Scholar
Leggett, G., & Harrington, I. (2019 ). The impact of project based learning (PBL) on students from low socioeconomic statuses: A review. International Journal of Inclusive Education, 25(11), 12701286. doi:10.1080/13603116.2019.1609101Google Scholar
Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 371387). New York, NY: Cambridge University Press.Google Scholar
MacDonald, R., Miller, E., & Lord, S. (2017). Doing and talking science: Engaging ELs in the discourse of the science and engineering practices. In Oliveira, A. W. & Weinburgh, M. H. (Eds.), Science teacher preparation in content-based second language acquisition (pp. 179197). Cham, Switzerland: Springer.Google Scholar
Marx, R. W., Blumenfeld, P. C., Krajcik, J. S., et al. (2004). Inquiry‐based science in the middle grades: Assessment of learning in urban systemic reform. Journal of Research in Science Teaching, 41(10), 10631080.Google Scholar
McNeill, K. L. (2009). Teachers’ use of curriculum to support students in writing scientific arguments to explain phenomena. Science Education, 93(2), 233268.Google Scholar
McNeill, K. L., & Krajcik, J. S. (2008). Middle school students’ use of appropriate and inappropriate evidence in writing scientific explanations. In Lovet, M. & Shah, P. (Eds.), Thinking with data (pp. 233265). New York, NY: Taylor & Francis.Google Scholar
McNeill, K. L., & Krajcik, J. S. (2012). Supporting grade 5–8 students in constructing explanations in science: The claim, evidence and reasoning framework for talk and writing. New York, NY: Pearson Allyn & Bacon.Google Scholar
Miller, E. C., & Krajcik, J. S. (2019). Promoting deep learning through project-based learning: A design problem. Disciplinary and Interdisciplinary Science Education Research, 1, Article 7. doi:10.1186/s43031–019-0009-6Google Scholar
National Academies of Sciences, Engineering, and Medicine. (2018). How people learn II: Learners, contexts, and cultures. Washington, DC: The National Academies Press. doi:10.17226/24783Google Scholar
National Academies of Sciences, Engineering, and Medicine. (2019). Science and engineering for grades 6–12: Investigation and design at the Center. Washington, DC: The National Academies Press.Google Scholar
National Research Council. (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: The National Academies Press.Google Scholar
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.Google Scholar
NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press. Retrieved from www.nextgenscience.org/next-generation-science-standardsGoogle Scholar
Nordine, J., & Lee, O. (Eds.). (2021). Crosscutting concepts: Strengthening science and engineering learning. Arlington, VA: National Science Teaching Association Press.Google Scholar
Novak, A. M., & Krajcik, J. S. (2019). A case study of project‐based learning of middle school students exploring water quality. In Moallem, M., Hung, W., & Dabbagh, N. (Eds.), The Wiley handbook of problem‐based learning (pp. 551572). Hoboken, NJ: John Wiley & Sons.Google Scholar
Novak, A. M., & Treagust, D. F. (2018). Adjusting claims as new evidence emerges: Do students incorporate new evidence into their scientific explanations? Journal of Research in Science Teaching, 55(3), 526549. doi:10.1002/tea.21429Google Scholar
Organisation for Economic Co-operation and Development. (2019). PISA 2018 results (Vol. I): What students know and can do. Paris, France: OECD Publishing. doi:10.1787/5f07c754-enGoogle Scholar
Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press.Google Scholar
Pellegrino, J. W., & Hilton, M. L. (2012). Developing transferable knowledge and skills in the 21st century. Washington, DC: National Research Council.Google Scholar
Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20(3), 29.Google Scholar
Schneider, B., Krajcik, J., Lavonen, J., & Salmela-Aro, K. (2020). Learning science: Crafting engaging science environments. New Haven, CT; London, England: Yale University Press.Google Scholar
Schneider, B., Krajcik, J., Lavonen, J., et al. (2020). Improving science achievement – Is it possible? Evaluating the efficacy of a high school chemistry and physics project-based learning intervention: Crafting engaging science environments [Under review]. Educational Researcher.Google Scholar
Schwarz, C., Passmore, C., & Reiser, B. J. (Eds.). (2016). Helping students make sense of the world using next generation science and engineering practices. Arlington, VA: National Science Teachers Association.Google Scholar
Schwarz, C., Reiser, B., Davis, E., et al. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(1), 232254.Google Scholar
Smith, C. L., Wiser, M., Anderson, C. W., & Krajcik, J. (2006). Implications of research on children’s learning for standards and assessment: A proposed learning progression for matter and the atomic molecular theory. Measurement: Interdisciplinary Research and Perspectives, 14(1 & 2), 198.Google Scholar
Williams, M., & Linn, M. (2003). WISE inquiry in fifth grade biology. Research in Science Education, 32(4), 145436.Google Scholar

References

Ainley, M., & Ainley, J. (2020). Motivation and learning. In Renninger, K. A. & Hidi, S. E. (Eds.), The Cambridge handbook of motivation and learning (pp. 665688). Cambridge, England: Cambridge University Press.Google Scholar
Azevedo, R. (2014). Metacognition and multimedia learning. In Mayer, R. E. (Ed.), The Cambridge handbook of multimedia (2nd ed., pp. 647672). Cambridge, England: Cambridge University Press.Google Scholar
Azevedo, R. (2020). Reflections on the field of metacognition: Issues, challenges, and opportunities. Metacognition and Learning, 15(2), 919998.Google Scholar
Azevedo, R., & Gasević, D. (2019). Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges. Computers in Human Behavior, 96, 207210.Google Scholar
Azevedo, R., Johnson, A., Chauncey, A., & Graesser, A. (2011). Use of hypermedia to convey and assess self-regulated learning. In Zimmerman, B. & Schunk, D. (Eds.), Handbook of self-regulation of learning and performance (pp. 102121). New York, NY: Routledge.Google Scholar
Azevedo, R., Taub, M., & Mudrick, N. V. (2018). Using multi-channel trace data to infer and foster self-regulated learning between humans and advanced learning technologies. In Schunk, D. & Greene, J. A. (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 254270). New York, NY: Routledge.Google Scholar
Baars, M., & Wijnia, L. (2018). The relation between task-specific motivational profiles and training of self-regulated learning skills. Learning and Individual Differences, 64, 125137.Google Scholar
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 126.Google Scholar
Biswas, G., Baker, R., & Paquette, L. (2018). Data mining for assessing self-regulated learning. In Schunk, D. & Greene, J. A. (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 388404). New York, NY: Routledge.Google Scholar
Biswas, G., Segedy, J. R., & Bunchongchit, K. (2016). From design to implementation to practice – a learning by teaching system: Betty’s Brain. International Journal of Artificial Intelligence in Education, 26(1), 350364.Google Scholar
Boekaerts, M. (1995). Self-regulated learning: Bridging the gap between metacognitive and metamotivation theories. Educational Psychologist, 30(4), 195200.Google Scholar
Bol, L., & Hacker, D. (2012). Calibration research: Where do we go from here? Frontiers in Psychology, 3, 16.Google Scholar
Bol, L., Hacker, D. J., Walck, C. C., & Nunnery, J. (2012). The effects of individual or group guidelines on the calibration accuracy and achievement of high school biology students. Contemporary Educational Psychology, 37(4), 280287.Google Scholar
Bol, L., Riggs, R., Hacker, D. J., & Nunnery, J. (2010). The calibration accuracy of middle school students in math classes. Journal of Research in Education, 21(2), 8196.Google Scholar
Butler, D., & Winne, P. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245281.Google Scholar
Carpenter, S. K. (2020). Distributed practice or spacing effect. In Zhang, L.-F. (Ed.), Oxford research encyclopedia of education. Oxford, England: Oxford University Press. Retrieved from https://664ef278-1723-43c6-bbe1-3bd4e65a87fe.filesusr.com/ugd/f4b9f1_18eca7ddc3bc4783a23bda3ff7e4d1f8.pdfGoogle Scholar
Cromley, J., & Azevedo, R. (2011). Measuring strategy use in context with multiple-choice items. Metacognition and Learning, 6(2), 155177.Google Scholar
Cromley, J., & Kunze, A. (2020). Metacognition in education: Translational research. Translational Issues in Psychological Science, 6(1), 1520.Google Scholar
Desoete, A. (2008). Multi-method assessment of metacognitive skills in elementary school children: How you test is what you get. Metacognition and Learning, 3, 189206.Google Scholar
Dignath, C., & Buttner, G. (2018). Teachers’ direct and indirect promotion of self-regulated learning in primary and secondary school mathematics classes – insights from video-based classroom observations and teacher interviews. Metacognition and Learning, 13(2), 127157.Google Scholar
Dignath, C., & Veenman, M. (2021). The role of direct strategy instruction and indirect activation of self-regulated learning. Evidence from classroom observation studies. Educational Psychology Review, 33, 489533.Google Scholar
Donker, A. S., de Boer, H., Kostons, D., Dignath van Ewijk, C. C., & van der Werf, M. P. C. (2014). Effectiveness of learning strategy instruction on academic performance: A meta-analysis. Educational Research Review, 11(1), 126.Google Scholar
Double, K., & Birney, D. (2019). Reactivity to measures of metacognition. Frontiers in Psychology, 10, 112.Google Scholar
Dunlosky, J., & Ariel, R. (2011). Self-regulated learning and the allocation of study time. In Ross, B. (Ed.), Psychology of learning and motivation (Vol. 54, pp. 103140). San Diego, CA: Elsevier Academic Press.Google Scholar
Dunlosky, J., & Lipko, A. (2007). Metacomprehension: A brief history and how to improve its accuracy. Current Directions in Psychological Science, 16(4), 228232.Google Scholar
Dunlosky, J., & Rawson, K. A. (2012). Overconfidence produces underachievement: Inaccurate self-evaluations undermine students’ learning and retention. Learning and Instruction, 22(4), 271280.Google Scholar
Dunlosky, J., & Rawson, K. (2019). The Cambridge handbook of cognition and education. Cambridge, England: Cambridge University Press.Google Scholar
Dunlosky, J., & Tauber, S. K. (2016). The Oxford handbook of metamemory. Oxford, England: Oxford University Press.Google Scholar
Dunn, K. E., & Lo, W.-J. (2015). Understanding the influence of learners’ forethought on their use of science study strategies in postsecondary science learning. International Journal of Science Education, 37(16), 25972618.Google Scholar
Elliot, A. J., & Hulleman, C. S. (2017). Achievement goals. In Elliot, A. J., Dweck, C. S., & Yeager, D. S. (Eds.), Handbook of competence and motivation: Theory and application (pp. 4360). New York, NY: The Guilford Press.Google Scholar
Fiechter, J. L., Benjamin, A. S., & Unsworth, N. (2016). The metacognitive foundations of effective remembering. In Dunlosky, J. & Tauber, S. K. (Eds.), The Oxford handbook of metamemory (pp. 307324). Oxford, England: Oxford University Press.Google Scholar
Flavell, J. H., Friedrichs, A. G., & Hoyt, J. D. (1970). Developmental changes in memorization processes. Cognitive Psychology, 1(4), 324340.Google Scholar
Garner, R. (1990). When children and adults do not use learning strategies: Toward a theory of settings. Review of Educational Research, 60(4), 517529.Google Scholar
Gentner, N., & Seufert, T. (2020). The double-edged interactions of prompts and self-efficacy. Metacognition and Learning, 15, 261289.Google Scholar
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451482.Google Scholar
Graesser, A. C. (2019). Learning science principles and technologies with agents that promote deep learning. In Feldman, R. S. (Ed.), Learning science: Theory, research, and practice (pp. 233). New York, NY: McGraw-Hill.Google Scholar
Greene, J. A., & Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of sophisticated mental models. Contemporary Educational Psychology, 34(1), 1829.Google Scholar
Greene, J. A., Deekens, V., Copeland, D., & Yu, S. (2018). Capturing and modeling self-regulated learning using think-aloud protocols. In Schunk, D. & Greene, J. A (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 323337). New York, NY: Routledge.Google Scholar
Greene, J. A., Hutchison, L. A., Costa, L., & Crompton, H. (2012). Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic. Contemporary Educational Psychology, 37(4), 307230.Google Scholar
Griffin, T. D., Mielicki, M. K., & Wiley, J. (2019). Improving students’ metacomprehension accuracy. In Dunlosky, J. and Rawson, K. A. (Eds.), The Cambridge handbook of cognition and education (pp. 619646). Cambridge, England: Cambridge University Press.Google Scholar
Hacker, D., & Bol, L. (2019). Calibration and self-regulated learning. In Dunlosky, J. & Rawson, K. (Eds.), The Cambridge handbook of cognition and education (pp. 647677). Cambridge, England: Cambridge University Press.Google Scholar
Hacker, D. J., Bol, L., & Bahbahani, K. (2008). Explaining calibration in classroom contexts: The effects of incentives, reflection, and attributional style. Metacognition and Learning, 3(2), 101121.Google Scholar
Hacker, D. J., Bol, L., Horgan, D., & Rakow, E. A. (2000). Test prediction and performance in a classroom context. Journal of Educational Psychology, 92(1), 160170.Google Scholar
Hart, J. T. (1965). Memory and the feeling-of-knowing experience. Journal of Educational Psychology, 56(4), 208216.Google Scholar
Hart, J. T. (1967). Memory and the memory-monitoring process. Journal of Verbal Learning and Verbal Behavior, 6(5), 685691.Google Scholar
Hartman, H. J. (2001). Metacognition in learning and instruction: Theory, research and practice. Amsterdam, The Netherlands: Springer.Google Scholar
Hartwig, M. K., & Dunlosky, J. (2017). Category learning judgments in the classroom: Can students judge how well they know course topics? Contemporary Educational Psychology, 49, 8090.Google Scholar
Ikeda, K., Yue, C. L., Murayama, K., & Castel, A. D. (2016). Achievement goals affect metacognitive judgments. Motivation Science, 2(4), 199219.Google Scholar
Jacobs, J. E., & Paris, S. G. (1987). Children’s metacognition about reading: Issues in definition, measurement, and instruction. Educational Psychologist, 22(3–4), 255278.Google Scholar
Järvelä, S., Malmberg, J., Haataja, E., Sobosincki, M., & Kirschner, P. (2021). What multimodal data can tell us about the students’ regulation of their learning process? Learning and Instruction, 72. doi:10.1016/j.learninstruc.2019.04.004Google Scholar
Jemstedt, A., Schwartz, B., & Jönsson, F. (2018). Ease-of-learning judgments are based on both processing fluency and beliefs. Memory, 26(6), 807815.Google Scholar
Kirk, E. P., & Ashcraft, M. H. (2001). Telling stories: The perils and promise of using verbal reports to study math strategies. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(1), 157175.Google Scholar
Kleitman, S., & Narciss, S. (2019). Introduction to the special issue “applied metacognition: Real-world applications beyond learning.” Metacognition & Learning, 14(3), 335342.Google Scholar
Koriat, A. (1997). Monitoring one’s own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126(4), 349370.Google Scholar
Koriat, A., & Bjork, R. A. (2006). Illusions of competence during study can be remedied by manipulations that enhance learners’ sensitivity to retrieval conditions at test. Memory & Cognition, 34(5), 959972.Google Scholar
Koriat, A., Ma’ayan, H., & Nussinson, R. (2006). The intricate relationship between monitoring and control in metacognition: Lessons for the cause-and effect relation between subjective experience and behavior. Journal of Experimental Psychology: General, 135(1), 3669.Google Scholar
Kramarski, B. (2018). Teachers as agents in prompting students’ SRL and performance: Applications for teachers’ dual role training program. In Schunk, D. & Greene, J. A. (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 223239). New York, NY: Routledge.Google Scholar
Kramarski, B., & Dudai, V. (2009). Group-metacognitive support for online inquiry in mathematics with differential self-questioning. Journal of Educational Computing Research, 40(4), 377404.Google Scholar
Lippmann, M., Schwartz, N., Jacobson, N., & Narciss, S. (2019). The concreteness of titles affects metacognition and study motivation. Instructional Science, 47(2), 257277.Google Scholar
Maki, R. H., & Serra, M. (1992). The basis of test predictions for text material. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(1), 116126.Google Scholar
Maki, R. H., Shields, M., Wheeler, A. E., & Zacchilli, T. L. (2005). Individual differences in absolute and relative metacomprehension accuracy. Journal of Educational Psychology, 97(4), 723731.Google Scholar
McDowell, L. D. (2019). The roles of motivation and metacognition in producing self-regulated learners of college physical science: A review of empirical studies. International Journal of Science Education, 41(17), 25242541.Google Scholar
Metcalfe, J., & Finn, B. (2008). Evidence that judgments of learning are causally related to study choice. Psychonomic Bulletin & Review, 15(1), 174179.Google Scholar
Metcalfe, J., & Kornell, N. (2005). A region of proximal learning model of study time allocation. Journal of Memory and Language, 52(4), 463477.Google Scholar
Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure of behavior. New York, NY: Holt, Rinehart & Winston.Google Scholar
Mudrick, N. V., Azevedo, R., & Taub, M. (2019). Integrating metacognitive judgements and eye movements using sequential pattern mining to understand processes underlying successful multimedia learning. Computers in Human Behavior, 96, 223234.Google Scholar
Muis, K., & Duffy, M. (2013). Epistemic climate and epistemic change: Instruction designed to change students’ epistemic beliefs and learning strategies and improve achievement. Journal of Educational Psychology, 105, 213222.Google Scholar
National Academies of Sciences, Engineering, and Medicine. (2018). How people learn II: Learners, contexts, and cultures. Washington, DC: The National Academies Press.Google Scholar
Nelson, T. O. (1996). Gamma is a measure of the accuracy of predicting performance on one item relative to another item, not of the absolute performance on an individual item. Applied Cognitive Psychology, 10(3), 257260.Google Scholar
Nietfeld, J. L., Enders, C. K., & Schraw, G. (2006). A Monte Carlo comparison of two measures of monitoring accuracy. Educational and Psychological Measurement, 66(2), 258271.Google Scholar
Pesout, O., & Nietfeld, J. (2020). The impact of cooperation and competition on metacognitive monitoring in classroom context. The Journal of Experimental Education, 1–22. doi:10.1080/00220973.2020.1751577Google Scholar
Pieschl, S., Stahl, E., Murray, T., & Bromme, R. (2013). Is adaptation to task complexity really beneficial for performance? Learning and Instruction, 22(4), 281289.Google Scholar
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Boekaerts, M., Pintrich, P., & Zeidner, M. (Eds.), Handbook of self-regulation (pp. 451502). San Diego, CA: Academic Press.Google Scholar
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667686.Google Scholar
Pintrich, P. R., Smith, D., Garcia, T., & McKeachie, W. (1993). Predictive validity and reliability of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53, 801813.Google Scholar
Robey, A., Dougherty, M., & Buttaccio, D. (2017). Making retrospective confidence judgements improves learners’ ability to decide what not to study. Psychological Science, 28(11), 16831693.Google Scholar
Roth, A., Ogrin, S., & Schmitz, B. (2015). Assessing self-regulated learning in higher education: A systematic literature review of self-report instruments. Educational Assessment, Evaluation and Accountability, 28(3), 225250. doi:10.1007/s11092-015-9229-2Google Scholar
Schraw, G. (2009a). A conceptual analysis of five measures of metacognitive monitoring. Metacognition and Learning, 4(1), 3345.Google Scholar
Schraw, G. (2009b). Measuring metacognitive judgements. In Hacker, D. J., Dunlosky, J., & Graesser, A. C. (Eds.), Handbook of metacognition in education (pp. 415429). New York, NY: Routledge.Google Scholar
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460475.Google Scholar
Schraw, G., Potenza, M. T., & Nebelsick-Gullet, L. (1993). Constraints on the calibration of performance. Contemporary Educational Psychology, 18(4), 455463.Google Scholar
Schunk, D., & Greene, J. A. (2018). Handbook self-regulation of learning and performance (2nd ed.). New York, NY: Routledge.Google Scholar
Sitzmann, T., & Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: What we know and where we need to go. Psychological Bulletin, 137(3), 421444.Google Scholar
Suárez, J. M., & Fernández, A. P. (2011). Evaluación de las estrategias de autorregulación afectivo-motivacional de los estudiantes: Las EEMA-VS. Anales de Psicología, 27(2), 369380.Google Scholar
Taub, M., & Azevedo, R. (2019). How does prior knowledge influence fixations on and sequences of cognitive and metacognitive SRL processes during learning with an ITS? International Journal of Artificial Intelligence in Education, 29(1), 128.Google Scholar
Urdan, Y., & Kaplan, A. (2020). The origins, evolution, and future directions of achievement goal theory. Contemporary Educational Psychology, 61, Article 101862. doi:10.1016/j.cedpsych.2020.101862Google Scholar
Veenman, M. J. (2007). The assessment and instruction of self-regulation in computer-based environments: A discussion. Metacognition and Learning, 2, 177183.Google Scholar
Weber, E. U., & Johnson, E. J. (2009). Mindful judgment and decision making. Annual Review of Psychology, 60(1), 5385.Google Scholar
Wiedbusch, M., & Azevedo, R. (2020). Modeling metacomprehension monitoring accuracy with eye gaze on informational content in a multimedia learning environment. In Symposium on Eye Tracking Research and Applications (ETRA’20 Full Papers), ACM, New York.Google Scholar
Winne, P. H. (2010a). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267276.Google Scholar
Winne, P. H. (2010b). Bootstrapping learner’s self-regulated learning. Psychological Test and Assessment Modeling, 52(4), 472490.Google Scholar
Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In Zimmerman, B. J. & Schunk, D. H. (Eds.), Handbook of self-regulation of learning and performance (pp. 1532). New York, NY: Routledge.Google Scholar
Winne, P. H. (2018a). Theorizing and researching levels of processing in self-regulated learning. British Journal of Educational Psychology, 88(4), 920.Google Scholar
Winne, P. H. (2018b). Cognition and metacognition within self-regulated learning. In Schunk, D. & Greene, J. (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 3648). New York, NY: Routledge.Google Scholar
Winne, P. H. (2020a). A proposed remedy for grievances about self-report methodologies. Frontline Learning Research, 8(3), 165174.Google Scholar
Winne, P. H. (2020b). Construct and consequential validity for learning analytics based on trace data. Computers in Human Behavior, 12, Article 106457. doi:10.1016/j.chb.2020.106457Google Scholar
Winne, P. H., Gupta, L., & Nesbit, J. (1994). Exploring individual differences in studying strategies using graph theoretic statistics. Alberta Journal of Educational Research, 40(2), 177193.Google Scholar
Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In Schunk, D. H. & Zimmerman, B. J. (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297314). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Winne, P. H., & Marzouk, Z. (2019). Learning strategies and self-regulated learning. In Dunlosky, J. & Rawson, K. (Eds.), Cambridge handbook of cognition and education (pp. 696715). New York, NY: Cambridge University Press.Google Scholar
Winne, P. H., Teng, K., Chang, D., et al. (2019). nStudy: Software for learning analytics about processes for self-regulated learning. Journal of Learning Analytics, 6(2), 95106.Google Scholar
Zepeda, C., Richey, J. E., Ronevich, P., & Nokes-Malach, T. (2015). Direct instruction of metacognition benefits adolescent science learning, transfer, and motivation: An in vivo study. Journal of Educational Psychology, 107(4), 954970.Google Scholar
Zhou, M. (2013). University student’s goal profiles and metacomprehension accuracy. Educational Psychology: An International Journal of Experimental Educational Psychology, 33(1), 113.Google Scholar
Zimmerman, B. (2011). Motivational sources and outcomes of self-regulated learning and performance. In Zimmerman, B. J. & Schunk, D. H. (Eds.), Handbook of self-regulation of learning and performance (pp. 4964). New York, NY: Routledge.Google Scholar
Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In Hacker, D., Dunlosky, J., & Graesser, A. (Eds.), Handbook of metacognition in education (pp. 299315). New York, NY: Routledge.Google Scholar
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Handbook of self-regulation of learning and performance. New York, NY: Routledge.Google Scholar

References

Amin, T. (2018). Representation, concepts, and concept learning. In Amin, T. & Levrini, O. (Eds.), Converging perspectives on conceptual change [eBook]. London, England: Routledge.Google Scholar
Azevedo, F. S., diSessa, A. A., & Sherin, B. (2012). An evolving framework for describing student engagement in classroom activities. Journal of Mathematical Behavior, 31(2), 270289.Google Scholar
Bascandziev, I., Tardiff, N., Zaitchik, D., & Carey, S. (2018). The role of domain-general cognitive resources in children’s construction of a vitalist theory of biology. Cognitive Psychology, 104(1–2), 128.Google Scholar
Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: MIT Press/Bradford Books.Google Scholar
Carey, S. (1991). Knowledge acquisition: Enrichment or conceptual change? In Carey, S. & Gelman, R. (Eds.), The epigenesis of mind (pp. 257291). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Carey, S. (2009). The origin of concepts. Oxford, England: Oxford University Press.Google Scholar
Chi, M. T. H. (1992). Conceptual change across ontological categories: Examples from learning and discovery in science. In Giere, F. (Ed.), Cognitive models of science: Minnesota studies in the philosophy of science (pp. 129160). Minneapolis, MN: University of Minnesota Press.Google Scholar
Clement, J. (1982). Students’ preconceptions in introductory mechanics. American Journal of Physics, 50(1), 6671.Google Scholar
diSessa, A. A. (1983). Phenomenology and the evolution of intuition. In Gentner, D. & Stevens, A. (Eds.), Mental models (pp. 1533). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
diSessa, A. A. (1996). What do “just plain folk” know about physics? In Olson, D. R. & Torrance, N. (Eds.), The handbook of education and human development: New models of learning, teaching, and schooling (pp. 709730). Oxford, England: Blackwell Publishers, Ltd.Google Scholar
diSessa, A. A. (2004). Meta-representation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293331.Google Scholar
diSessa, A. A. (2017). Conceptual change in a microcosm: Comparative analysis of a learning event. Human Development, 60(1), 137.Google Scholar
diSessa, A. A., Levin, M., & Brown, N. (Eds.). (2016). Knowledge and interaction: A synthetic agenda for the learning sciences. New York, NY: Routledge.Google Scholar
diSessa, A. A., Sherin, B., & Levin, M. (2016). Knowledge analysis: An introduction. In diSessa, A., Levin, M., & Brown, N. (Eds.), Knowledge and interaction: A synthetic agenda for the learning sciences (pp. 3071). New York, NY: Routledge.Google Scholar
diSessa, A. A., & Wagner, J. F. (2005). What coordination has to say about transfer. In Mestre, J. (Ed.), Transfer of learning from a modern multi-disciplinary perspective (pp. 121154). Greenwich, CT: Information Age Publishing.Google Scholar
Driver, R. (1989). Students’ conceptions and the learning of science. International Journal of Science Education, 11(5), 481490.Google Scholar
Gruber, H., & Voneche, J. (1977). The essential Piaget. New York, NY: Basic Books.Google Scholar
Hammer, D., & Elby, A. (2002). On the form of a personal epistemology. In Hofer, B. & Pintrich, P. (Eds.), Personal epistemology (pp. 169190). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Harvard-Smithsonian Center for Astrophysics. (1987). A private universe [Video]. Annenberg/CPB. www.learner.org/series/a-private-universe/1-a-private-universe/.Google Scholar
Hawkins, D. (1978). Critical barriers to science learning. Outlook, 29, 323.Google Scholar
Hewson, P., & Hennessey, M. G. (1992). Making status explicit: A case study of conceptual change. In Duit, R., Goldberb, F., & Niedderer, H. (Eds.), Research in physics learning: Theoretical and empirical studies (pp. 176187). Kiel, Germany: IPN.Google Scholar
Hunt, E., & Minstrell, J. (1994). A cognitive approach to the teaching of physics. In McGilly, K. (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 5174). Cambridge, MA: MIT Press.Google Scholar
Inagaki, K., & Hatano, G. (2002). Young children’s naive thinking about the biological world. New York, NY: Psychology Press.Google Scholar
Ioannides, C., & Vosniadou, C. (2002). The changing meanings of force. Cognitive Science Quarterly, 2(1), 561.Google Scholar
Izsák, A. (2000). Inscribing the winch: Mechanisms by which students develop knowledge structures for representing the physical world with algebra. Journal of the Learning Sciences, 9(1), 3174.Google Scholar
Kapon, S., & diSessa, A. A. (2012). Reasoning through instructional analogies. Cognition and Instruction, 30(3), 261310.Google Scholar
Karmiloff-Smith, A. (1988). The child as a theoretician, not an inductivist. Mind and Language, 3(3), 183195.Google Scholar
Keil, F. (1994). The birth and nurturance of concepts by domains: The origins of concepts of living things. In Hirschfield, L. & Gelman, S. (Eds.), Mapping the mind: Domain specificity in cognition and culture (pp. 234254). Cambridge, England: Cambridge University Press.Google Scholar
Kuhn, T. S. (1970). The structure of scientific revolutions (2nd ed.). Chicago, IL: University of Chicago Press.Google Scholar
Lakatos, I. (1970). Falsification and the methodology of scientific research programmes. In Lakatos, I. & Musgrave, A. (Eds.), Criticism and the growth of knowledge (pp. 91195). London, England; New York, NY: Cambridge University Press.Google Scholar
Levin, M. (2018). Conceptual and procedural knowledge during strategy construction: A complex knowledge systems perspective. Cognition and Instruction, 36(3), 247278.Google Scholar
Levrini, O., Levin, M., & Fantini, P. (2018). Personal, deeply affective, and aesthetic engagement with science content: When disciplinary learning becomes a vehicle for identity construction. In Amin, T. & Levrini, O. (Eds.), Converging perspectives on conceptual change [eBook]. London, England: Routledge.Google Scholar
McCloskey, M. (1983). Naive theories of motion. In Gentner, D. & Stevens, A. (Eds.), Mental models (pp. 299323). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Minstrell, J. (1982). Explaining the “at rest” condition of an object. The Physics Teacher, 20(1), 1014.Google Scholar
Minstrell, J. (1989). Teaching science for understanding. In Resnick, L. & Klopfer, L. (Eds.), Toward the thinking curriculum (pp. 129149). Alexandria, VA: Association for Supervision and Curriculum Development.Google Scholar
Nersessian, N. (1992). How do scientists think? In Giere, F. (Ed.), Cognitive models of science: Minnesota studies in the philosophy of science (pp. 344). Minneapolis, MN: University of Minnesota Press.Google Scholar
Parnafes, O. (2007). What does “fast” mean? Understanding the physical world through computational representations. Journal of the Learning Sciences, 16(3), 415450.Google Scholar
Pfundt, H, & Duit, R. (1988). Bibliography: Students’ alternative frameworks and science education (2nd ed.). Kiel, Germany: IPN.Google Scholar
Philip, T. M. (2011). An “ideology in pieces” approach to studying change in teachers’ sensemaking about race, racism and racial justice. Cognition and Instruction, 29(3), 297329.Google Scholar
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211227.Google Scholar
Sherin, B. (2001). A comparison of programming languages and algebraic notation as expressive languages for physics. International Journal of Computers for Mathematical Learning, 6(1), 161.Google Scholar
Sinatra, G. (2004). The “warming trend” in conceptual change research: The legacy of Paul R. Pintrich. Educational Psychologist, 40(2), 107115.Google Scholar
Smith, C., Maclin, D., Grosslight, L., & Davis, H. (1997). Teaching for understanding: A study of students’ preinstruction theories of matter and a comparison of the effectiveness of two approaches to teaching about matter and density. Cognition and Instruction, 15(3), 317393.Google Scholar
Smith, J. P., diSessa, A. A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3(2), 115163.Google Scholar
Strike, K. A., & Posner, G. J. (1990). A revisionist theory of conceptual change. In Duschl, R. & Hamilton, R. (Eds.), Philosophy of science, cognitive science, and educational theory and practice (pp. 147176). Albany, NY: SUNY Press.Google Scholar
Tiberghien, A. (1980). Modes and conditions of learning: The learning of some aspects of the concept of heat. In Archenhold, W., Driver, R., Orton, A., & Wood-Robinson, C. (Eds.), Cognitive development research in science and mathematics: Proceedings of an international symposium (pp. 288309). University of Leeds.Google Scholar
Toulmin, S. (1972). Human understanding (Vol. 1). Oxford, England: Clarendon Press.Google Scholar
Viennot, L. (1979). Spontaneous reasoning in elementary dynamics. European Journal of Science Education, 1(2), 205221.Google Scholar
Wellman, H., & Gelman, S. (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43(1), 337375.Google Scholar
Wiser, M. (1995). Use of history of science to understand and remedy students’ misconceptions about heat and temperature. In Perkins, D., Schwartz, J., West, M., & Wiske, M. (Eds.), Software goes to school: Teaching for understanding with new technologies (pp. 2338). Oxford, England: Oxford University Press.Google Scholar
Wiser, M., & Amin, T. (2002). Microworld-based interactions for conceptual change in science. In Limón, M. & Mason, L. (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 357388). Dordrecht, The Netherlands: Kluwer Academic Publishers.Google Scholar
Wiser, M., & Carey, S. (1983). When heat and temperature were one. In Gentner, D. & Stevens, A. (Eds.), Mental models (pp. 267298). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Wiser, M., & Smith, C. (2016). How is conceptual change possible? Insights from science education [eBook]. In Barner, D. & Baron, A. (Eds.), Core knowledge and conceptual change. Oxford Scholarship Online.Google Scholar

References

Abrahamson, D., Trninic, D., Gutiérrez, J. F., Huth, J., & Lee, R. G. (2011). Hooks and shifts: A dialectical study of mediated discovery. Technology, Knowledge and Learning, 16(1), 5585.Google Scholar
Akkerman, S., & Bruining, T. (2016). Multilevel boundary crossing in a professional development school partnership. Journal of the Learning Sciences, 25(2), 240284.Google Scholar
Bal, A., Afacan, K., & Cakir, H. I. (2018). Culturally responsive school discipline: Implementing Learning Lab at a high school for systemic transformation. American Educational Research Journal, 55(5), 10071050.Google Scholar
Bellack, A., Kliebard, H., Hyman, R., & Smith, F. (1966). The language of the classroom. New York, NY: Teachers College Press.Google Scholar
Bowers, J., Cobb, P., & McClain, K. (1999). The evolution of mathematical practices: A case study. Cognition and Instruction, 17(1), 2566.Google Scholar
Bruner, J. S. (1973). Beyond the information given: Studies in the psychology of knowing. New York, NY: Norton.Google Scholar
Caldwell, H., Krinsky, J., Brunila, M., & Ranta, K. (2019). Learning to common, commoning as learning. ACME: An International Journal for Critical Geographies, 18(6), 12071233.Google Scholar
Clark, H. H., & Schaefer, E. F. (1989). Contributing to discourse. Cognitive Science, 13(2), 259294.Google Scholar
Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge, MA: Harvard University Press.Google Scholar
Cussins, A. (1992). Content, embodiment and objectivity: The theory of cognitive trails. Mind, 101(404), 651688.Google Scholar
Cussins, A. (1993). Nonconceptual content and the elimination of misconceived composites. Mind & Language, 8(2), 234252.Google Scholar
Davydov, V. V. (1990). Types of generalization in instruction: Logical and psychological problems in the structuring of school curricula. Reston, VA: National Council of Teachers of Mathematics.Google Scholar
Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki, Finland: Orienta-Konsultit.Google Scholar
Engeström, Y. (2001). Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work, 14(1), 133156.Google Scholar
Engeström, Y. (2015). Learning by expanding: An activity-theoretical approach to developmental research (2nd ed.). Cambridge, England: Cambridge University Press.Google Scholar
Engeström, Y. (2016). Studies in expansive learning: Learning what is not yet there. Cambridge, England: Cambridge University Press.Google Scholar
Engeström, Y. (2017). Expanding the scope of science education: An activity-theoretical perspective. In Hahl, K., Juuti, K., Lampiselkä, J., Lavonen, J., & Uitto, A. (Eds.), Cognitive and affective aspects in science education research (pp. 357370). New York, NY: Springer.Google Scholar
Engeström, Y. (2018). Expertise in transition: Expansive learning in medical work. Cambridge, England: Cambridge University Press.Google Scholar
Engeström, Y., Nummijoki, J., & Sannino, A. (2012). Embodied germ cell at work: Building an expansive concept of physical mobility in home care. Mind, Culture, and Activity, 19(3), 287309.Google Scholar
Engeström, Y., Puonti, A., & Seppänen, L. (2003). Spatial and temporal expansion of the object as a challenge for reorganizing work. In Nicolini, D., Gherardi, S., & Yanow, D. (Eds.), Knowing in organizations: A practice-based approach (pp. 151186). Armonk, NY: Sharpe.Google Scholar
Engeström, Y., Rantavuori, J., & Kerosuo, H. (2013). Expansive learning in a library: Actions, cycles and deviations from instructional intentions. Vocations and Learning, 6(1), 81106.Google Scholar
Engeström, Y., & Sannino, A. (2010). Studies of expansive learning: Foundations, findings and future challenges. Educational Research Review, 5(1), 124.Google Scholar
Engeström, Y., & Sannino, A. (2011). Discursive manifestations of contradictions in organizational change efforts. Journal of Organizational Change Management, 24(3), 368387.Google Scholar
Engeström, Y., & Sannino, A. (2012). Whatever happened to process theories of learning? Learning, Culture and Social Interaction, 1(1), 4556.Google Scholar
Engeström, Y., Sannino, A., & Virkkunen, J. (2014). On the methodological demands of formative interventions. Mind, Culture, and Activity, 21(2), 118128.Google Scholar
Engle, R. A. (2006). Framing interactions to foster generative learning: A situative explanation of transfer in a community of learners classroom. Journal of the Learning Sciences, 15(4), 451498.Google Scholar
Galleguillos, J., & de Carvalho Borba, M. (2018). Expansive movements in the development of mathematical modeling: Analysis from an Activity Theory perspective. ZDM, 50(1–2), 129142.Google Scholar
Ginnett, R. C. (1993). Crews as groups: Their formation and leadership. In Wiener, E. L., Kanki, B. G., & Helmreich, R. L. (Eds.), Cockpit resource management (pp. 7198). San Diego, CA: Academic Press.Google Scholar
Goodwin, C. (2018). Co-operative action. Cambridge, England: Cambridge University Press.Google Scholar
Greeno, J. G. (1991). Number sense as situated knowing in a conceptual domain. Journal for Research in Mathematics Education, 22(3), 170218.Google Scholar
Greeno, J. G. (2012). Concepts in activities and discourses. Mind, Culture, and Activity, 19(3), 310313.Google Scholar
Greeno, J. G. (2016). Cultural-historical activity theory/design-based research in Pasteur’s quadrant. Journal of the Learning Sciences, 25(4), 634639.Google Scholar
Greeno, J. G., & Engeström, Y. (2014). Learning in activity. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences (pp. 128147). New York, NY: Cambridge University Press.Google Scholar
Gutiérrez, K. D. (2020). When learning as movement meets learning on the move. Cognition and Instruction, 38(3), 427433.Google Scholar
Gutiérrez, K. D., Engeström, Y., & Sannino, A. (2016). Expanding educational research and interventionist methodologies. Cognition and Instruction, 34(3), 275284.Google Scholar
Gutiérrez, K. D., & Jurow, A. S. (2016). Social design experiments: Toward equity by design. Journal of the Learning Sciences, 25(4), 565598.Google Scholar
Gutiérrez, K., Rymes, B., & Larson, J. (1995). Script, counterscript, and underlife in the classroom: James Brown versus Brown v. Board of Education. Harvard Educational Review, 65(3), 445472.Google Scholar
Hall, R., & Greeno, J. G. (2008). Conceptual learning. In Good, T. (Ed.), 21st century education: A reference handbook (pp. 212221). London, England: Sage Publications.Google Scholar
Headrick Taylor, K. (2017). Learning along lines: Locative literacies for reading and writing the city. Journal of the Learning Sciences, 26(4), 533574.Google Scholar
Holland, D. C., Lachicotte Jr, W. S., Skinner, D., & Cain, C. (2001). Identity and agency in cultural worlds. Cambridge, MA: Harvard University Press.Google Scholar
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
Il’enkov, E. V. (1977). Dialectical logic: Essays in its history and theory. Moscow, Russia: Progress.Google Scholar
Ingold, T. (2007). Lines: A brief history. London, England: Routledge.Google Scholar
Jurow, A. S., Teeters, L., Shea, M., & Van Steenis, E. (2016). Extending the consequentiality of “invisible work” in the food justice movement. Cognition and Instruction, 34(3), 210221.Google Scholar
Knorr-Cetina, K. (1997). Sociality with objects: Social relations in postsocial knowledge societies. Theory, Culture & Society, 14(4), 130.Google Scholar
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, England: Cambridge University Press.Google Scholar
Lee, V. R., & Dubovi, I. (2020). At home with data: Family engagements with data involved in type 1 diabetes management. Journal of the Learning Sciences, 29(1), 1131.Google Scholar
Leont’ev, A. N. (1978). Activity, consciousness, and personality. Englewood Hills, NJ: Prentice-Hall.Google Scholar
Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 125.Google Scholar
Martínez-Álvarez, P. (2019). Redistribution of labor to prepare teachers to work in inclusive bilingual classrooms. Urban Education. doi:10.1177/0042085919873697Google Scholar
Marton, F., & Pang, M. F. (2006). On some necessary conditions of learning. Journal of the Learning Sciences, 15(2), 193220.Google Scholar
Marton, F., Tsui, A. B., Chik, P. P., Ko, P. Y., & Lo, M. L. (2004). Classroom discourse and the space of learning. London, England: Routledge.Google Scholar
Meléndez, J. W. (2020). Latino immigrants in civil society: Addressing the double-bind of participation for expansive learning in participatory budgeting. Journal of the Learning Sciences. doi:10.1080/10508406.2020.1807349Google Scholar
Mercer, N. (1995). The guided construction of knowledge: Talk amongst teachers and learners. Clevedon, England: Multilingual Matters.Google Scholar
Nersessian, N. J. (2012). Engineering concepts: The interplay between concept formation and modeling practices in bioengineering sciences. Mind, Culture, and Activity, 19(3), 222239.Google Scholar
Nolen, S. B., Wetzstein, L., & Goodell, A. (2020). Designing material tools to mediate disciplinary engagement in environmental science. Cognition and Instruction, 38(2), 179223.Google Scholar
Nummijoki, J., Engeström, Y., & Sannino, A. (2018). Defensive and expansive cycles of learning: A study of home care encounters. Journal of the Learning Sciences, 27(2), 224264.Google Scholar
O’Neill, D. K. (2016). Understanding design research–practice partnerships in context and time: Why learning sciences scholars should learn from cultural-historical activity theory approaches to design-based research. Journal of the Learning Sciences, 25(4), 497502.Google Scholar
Radford, L. (2020). Davydov’s concept of the concept and its dialectical materialist background. Educational Studies in Mathematics. doi:10.1007/s10649–020-09959-yGoogle Scholar
Resnick, L., Michaels, S., & O’Connor, C. (2010). How (well-structured) talk builds the mind. In Sternberg, R. & Preiss, D. (Eds.), From genes to context: New discoveries about learning from educational research and their applications (pp. 163194). New York, NY: Springer.Google Scholar
Rogoff, B. (2014). Learning by observing and pitching in to family and community endeavors: An orientation. Human Development, 57(2–3), 6981.Google Scholar
Rubel, L. H., Hall-Wieckert, M., & Lim, V. Y. (2017). Making space for place: Mapping tools and practices to teach for spatial justice. Journal of the Learning Sciences, 26(4), 643687.Google Scholar
Russ, R. S., & Berland, L. K. (2019). Invented science: A framework for discussing a persistent problem of practice. Journal of the Learning Sciences, 28(3), 279301.Google Scholar
Sannino, A. (2008). Sustaining a non-dominant activity in school: Only a utopia? Journal of Educational Change, 9(4), 329338.Google Scholar
Sannino, A. (2015). The principle of double stimulation: A path to volitional action. Learning, Culture and Social Interaction, 6(1), 115.Google Scholar
Sannino, A. (2020). Transformative agency as warping: How collectives accomplish change amidst uncertainty. Pedagogy, Culture & Society. doi:10.1080/14681366.2020.1805493Google Scholar
Sannino, A., Engeström, Y., & Lemos, M. (2016). Formative interventions for expansive learning and transformative agency. Journal of the Learning Sciences, 25(4), 599633.Google Scholar
Sawyer, R. K. (Ed.). (2014). The Cambridge handbook of the learning sciences (2nd ed.). New York, NY: Cambridge University Press.Google Scholar
Schatzki, T. R. (2010). The timespace of human activity: On performance, society, and history as indeterminate teleological events. Lanham, MD: Lexington Books.Google Scholar
Schegloff, E. A. (2007). Sequence organization in interaction: A primer in conversation analysis I. Cambridge, England: Cambridge University Press.Google Scholar
Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. Cambridge, England: Cambridge University Press.Google Scholar
Turner, J. C., Christensen, A., Kackar-Cam, H. Z., Fulmer, S. M., & Trucano, M. (2018). The development of professional learning communities and their teacher leaders: An activity systems analysis. Journal of the Learning Sciences, 27(1), 4988.Google Scholar
Van de Sande, C. C., & Greeno, J. G. (2012). Achieving alignment of perspectival framings in problem-solving discourse. Journal of the Learning Sciences, 21(1), 144.Google Scholar
Van Wart, S., Lanouette, K., & Parikh, T. S. (2020). Scripts and counterscripts in community-based data science: Participatory digital mapping and the pursuit of a third space. Journal of the Learning Sciences, 29(1), 127153.Google Scholar
Vossoughi, S. (2014). Social analytic artifacts made concrete: A study of learning and political education. Mind, Culture, and Activity, 21(4), 353373.Google Scholar
Vossoughi, S., Jackson, A., Chen, S., Roldan, W., & Escudé, M. (2020). Embodied pathways and ethical trails: Studying learning in and through relational histories. Journal of the Learning Sciences, 29(2), 183223.Google Scholar
Vygotsky, L. S. (1987). The collected works of L. S. Vygotsky: Vol. 1. Problems of general psychology. New York, NY: Plenum.Google Scholar
Wells, G. (1993). Reevaluating the IRF sequence: A proposal for the articulation of theories of activity and discourse for the analysis of teaching and learning in the classroom. Linguistics and Education, 5(1), 137.Google Scholar
White, T. (2019). Artifacts, agency and classroom activity: Materialist perspectives on mathematics education technology. Cognition and Instruction, 37(2), 169200.Google Scholar

References

Aronson, E. (1978). The jigsaw classroom. Beverly Hills, CA: Sage Publications.Google Scholar
Bielaczyc, K., & Collins, A. (1999). Learning communities in classrooms: A reconceptualization of educational practice. In Reigeluth, C. M. (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (pp. 269292). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Bransford, J. D., Brown, A. L., & Cocking, R. (2000). How people learn: Brain, mind, experience and school (expanded ed.) Washington, DC: National Academy Press.Google Scholar
Bransford, J. D., Franks, J. J., Vye, N. J., & Sherwood, R. D. (1989). New approaches to instruction: Because wisdom can’t be told. In Vosniadou, S. & Ortony, A. (Eds.), Similarity and analogical reasoning (pp. 470497). New York, NY: Cambridge University Press.Google Scholar
Brown, A., & Campione, J. (1996). Psychological theory and the design of innovative learning environments: On procedures, principles, and systems. In Schauble, L. & Glaser, R. (Eds.), Innovations in learning: New environments for education (pp. 289325). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 3242.Google Scholar
Burton, R., Brown, J. S., & Fischer, G. (1984). Skiing as a model of instruction. In Rogoff, B. & Lave, J. (Eds.), Everyday cognition: Its developmental and social context (pp. 139150). Cambridge, MA: Harvard University Press.Google Scholar
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145182.Google Scholar
Cobb, P., & Bauersfeld, H. (Eds.). (1995). The emergence of mathematical meaning: Interaction in classroom cultures. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Cognition and Technology Group at Vanderbilt. (1997). The Jasper Project: Lessons in curriculum, instruction, assessment, and professional development. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Collins, A., & Brown, J. S. (1988). The computer as a tool for learning through reflection. In Mandl, H. & Lesgold, A. (Eds.), Learning issues for intelligent tutoring systems (pp. 118). New York, NY: Springer.Google Scholar
Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the craft of reading, writing, and mathematics. In Resnick, L. B. (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453494). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Collins, A., & Smith, E. E. (1982). Teaching the process of reading comprehension. In Detterman, D. K. & Sternberg, R. J. (Eds.), How much and how can intelligence be increased? (pp. 173185). Norwood, NJ: Ablex.Google Scholar
Collins, A., & Stevens, A. L. (1983). A cognitive theory of interactive teaching. In Reigeluth, C. M. (Ed.), Instructional design theories and models: An overview (pp. 247278). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Cuban, L. (1984). How teachers taught. New York, NY: Longman.Google Scholar
Davis, E. A., & Miyake, N. (Eds.). (2004). Special issue: Scaffolding. Journal of the Learning Sciences, 13(3), 265451.Google Scholar
Gardner, H. (1989). Zero-based arts education: An introduction to Arts Propel. Studies in Art Education, 30(2), 7183.Google Scholar
Hatano, G., & Inagaki, K. (1991). Sharing cognition through collective comprehension activity. In Resnick, L., Levin, J., & Teasley, S. D. (Eds.), Perspectives on socially shared cognition (pp. 331348). Washington, DC: American Psychological Association.Google Scholar
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379424.Google Scholar
Kapur, M. (2012). Productive failure in learning the concept of variance. Instructional Science, 40(4), 651672.Google Scholar
Kapur, M. (2014). Productive failure in learning math. Cognitive Science, 38(5), 10081022. doi: 10.1111/cogs.12107Google Scholar
Kapur, M. (2015). The preparatory effects of problem solving versus problem posing on learning from instruction. Learning and Instruction, 39, 2331.Google Scholar
Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289299.Google Scholar
Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. Journal of the Learning Sciences, 21(1), 4583.Google Scholar
Kapur, M., & Rummel, N. (2012). Productive failure in learning and problem solving. Instructional Science, 40(4), 645650.Google Scholar
Kolodner, J. L., Camp, P. J., Crismond, D., et al. (2003). Problem-based learning meets case-based reasoning in the middle-school classroom: Putting learning by design into practice. Journal of the Learning Sciences, 12(4), 495547.Google Scholar
Lampert, M., Rittenhouse, P., & Crumbaugh, C. (1996). Agreeing to disagree: Developing sociable mathematical discourse. In Olson, D. & Torrance, N. (Eds.), Handbook of education and human development (pp. 731764). Oxford, England: Blackwell.Google Scholar
Lave, J. (1988). The culture of acquisition and the practice of understanding (Report No. IRL88–0007). Palo Alto, CA: Institute for Research on Learning.Google Scholar
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York, NY: Cambridge University Press.Google Scholar
Lepper, M. R., & Greene, D. (1979). The hidden costs of reward. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Loibl, K., Roll, I., & Rummel, N. (2017). Towards a theory of when and how problem solving followed by instruction supports learning. Educational Psychology Review, 29(4), 693715. doi:10.1007/s10648-016-9379-xGoogle Scholar
Norman, D. A. (1973). Memory, knowledge, and the answering of questions. In Solso, R. L. (Ed.), Contemporary issues in cognitive psychology: The Loyola symposium (pp. 135165). Washington, DC: Winston.Google Scholar
Nowakowski, A., Campbell, R., Monson, D., et al. (1994). Goal-based scenarios: A new approach to professional education. Educational Technology, 34(9), 332.Google Scholar
Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and monitoring activities. Cognition and Instruction, 1(2), 117175.Google Scholar
Quintana, C., Reiser, B. J., Davis, E. A., et al. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13(3), 337386.Google Scholar
Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273304.Google Scholar
Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88(3), 345372.Google Scholar
Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. Journal of the Learning Sciences, 3(3), 265283.Google Scholar
Scardamalia, M., Bereiter, C., & Steinbach, R. (1984). Teachability of reflective processes in written composition. Cognitive Science, 8(2), 173190.Google Scholar
Schank, R. C., Fano, A., Bell, B., & Jona, M. (1994). The design of goal-based scenarios. Journal of the Learning Sciences, 3(4), 305346.Google Scholar
Schoenfeld, A. H. (1985). Mathematical problem solving. Orlando, FL: Academic Press.Google Scholar
Schon, D. A. (1983). The reflective practitioner: How professionals think in action. New York, NY: Basic Books.Google Scholar
Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129184.Google Scholar
Sinha, T., & Kapur, M. (2021). When problem-solving followed by instruction works: Evidence for productive failure. Review of Educational Research, 91(5), 761798.Google Scholar
Sinha, T., Kapur, M., West, R., Catasta, M., Hauswirth, M., & Trninic, D. (2020). Differential benefits of explicit failure-driven and success-driven scaffolding in problem-solving prior to instruction. Journal of Educational Psychology. doi:10.1037/edu0000483Google Scholar
Smith, F. (1988). Joining the literacy club. Portsmouth, NH: Heinemann.Google Scholar
Stigler, J., & Hiebert, J. (1999). The teaching gap: Best ideas from the world’s teachers for improving education in the classroom. New York, NY: Free Press.Google Scholar
VanLehn, K., Siler, S., Murray, C., Yamauchi, T., & Baggett, W. B. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 21(3), 209249.Google Scholar
Vygotsky, L. S. (1978). Mind in society: The development of higher mental processes (Cole, M., John-Steiner, V., Scribner, S., & Souberman, E., Eds.), Cambridge, MA: Harvard University Press.Google Scholar
Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. New York, NY: Cambridge University Press.Google Scholar
White, B. Y. (1984). Designing computer games to help physics students understand Newton’s laws of motion. Cognition and Instruction, 1(1), 69108.Google Scholar
White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3118.Google Scholar
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry and Allied Disciplines, 17(2), 89100.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Foundations
  • Edited by R. Keith Sawyer, University of North Carolina, Chapel Hill
  • Book: The Cambridge Handbook of the Learning Sciences
  • Online publication: 14 March 2022
  • Chapter DOI: https://doi.org/10.1017/9781108888295.003
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Foundations
  • Edited by R. Keith Sawyer, University of North Carolina, Chapel Hill
  • Book: The Cambridge Handbook of the Learning Sciences
  • Online publication: 14 March 2022
  • Chapter DOI: https://doi.org/10.1017/9781108888295.003
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Foundations
  • Edited by R. Keith Sawyer, University of North Carolina, Chapel Hill
  • Book: The Cambridge Handbook of the Learning Sciences
  • Online publication: 14 March 2022
  • Chapter DOI: https://doi.org/10.1017/9781108888295.003
Available formats
×