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), 203–239.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), 247–286.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), 11–13.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. 133–162). 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), 86–107.CrossRefGoogle Scholar

Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.Google Scholar

Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.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), 363–392.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. 229–270). 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), 32–42.CrossRefGoogle Scholar

Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.CrossRefGoogle Scholar

Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19.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), 1–35.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), 516–535.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. 551–566). 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), 5–8.Google Scholar

Dillenbourg, P. (1999). What do you mean by collaborative learning?. In Dillenbourg, P. (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 1–19). 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. 651–660). 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), 367–383.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), 4–58.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. 335–354). 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), 347–378.CrossRefGoogle Scholar

Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000). Executive attention and metacognitive regulation. Consciousness and Cognition, 9(2), 288–307.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), 300–329.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. 67–82). 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), 41–50.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), 424–436.Google Scholar

Greeno, J. G. (1997). On claims that answer the wrong questions. Educational Researcher, 26(1), 5–17.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), 99–107.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), 112–119.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), 103–112.Google Scholar

Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4(1), 39–103.Google Scholar

Klahr, D. (2019). Learning sciences research and Pasteur’s quadrant. Journal of the Learning Sciences, 28(2), 153–159.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. 383–412). 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. 61–77). 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), 1–6.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. 243–288). 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), 318–337.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. 31–60). 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), 273–290.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), 445–452.CrossRefGoogle Scholar

Linn, M. C., & Slotta, J. D. (2000). WISE science. Educational Leadership, 58(2), 29–32.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), 1063–1080.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), 1–43.CrossRefGoogle Scholar

Nathan, M. J. (2012). Rethinking formalisms in formal education. Educational Psychologist, 47(2), 125–148.CrossRefGoogle Scholar

Nathan, M. J. (2014). Grounded mathematical reasoning. In Shapiro, L. (Ed.), The Routledge handbook of embodied cognition (pp. 171–183). 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), 329–345.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), 1925–1954.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), 151–171.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), 331–337.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. 307–335). 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), 60–67.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), 69–92.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), 4–13.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), 297–327.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), 387–431.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), 248–286.Google Scholar

Ur, S., & VanLehn, K. (1995). Steps: A simulated, tutorable physics student!. Journal of Artificial Intelligence in Education, 6(4), 405–437.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), 52–66.Google Scholar

Von Glasersfeld, E. (1989). Cognition, construction of knowledge, and teaching. Synthese, 80(1), 121–140.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), 167–183.Google Scholar

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), 45–72. 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. 115–138). 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), 271–311.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), 5–53. 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), 367–375. 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), 309–344. 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), 625–664.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. 73–93). 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), 1082–1112. 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), 369–398.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), 507–529. 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. 303–310). 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), 858–886. 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. 453–494). 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), 91–142.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. 107–128). 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), 63–103.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), 939–954.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), 226–239. 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), 355–385.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. 335–354). 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), 399–483.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), 567–589. 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), 41–58. 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), 39–68. 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. 117–138). 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. 15–46). 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. 25–51). Greenwich, CT: Information Age Publishing.Google Scholar

Guzdial, M. (1994). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1–44.CrossRefGoogle Scholar

Hermkes, R., Mach, H., & Minnameier, G. (2018). Interaction-based coding of scaffolding processes. Learning and Instruction, 54(1), 147–155. 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), 517–519. 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), 451–493.Google Scholar

Herrenkohl, L. R., Tasker, T., & White, B. (2011). Pedagogical practices to support classroom cultures of scientific inquiry. Cognition and Instruction, 29(1), 1–44.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), 247–298.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), 99–107.Google Scholar

Hutchins, E. (1996). Learning to navigate. In Chaiklin, S. & Lave, J. (Eds.), Understanding practice: Perspectives on activity and context (pp. 35–63). 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), 925–937.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), 45–83. 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. 61–96). 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), 159–185.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), 277–311. doi:10.1080/07370000903221718Google Scholar

Lajoie, S. P. (2005). Extending the scaffolding metaphor. Instructional Science, 33(5–6), 541–557.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. 63–82). 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), 97–141.Google Scholar

Lee, C. D. (2017). Expanding visions of how people learn: The centrality of identity repertoires. Journal of the Learning Sciences, 26(3), 517–524. 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), 530–542.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, 209–222. 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. 153–196). 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. 75–105). 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. 315–340). 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), 517–538.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), 69–98. 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), 236–266.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), 416–460. 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), 315–372.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), 50–110.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), 283–297.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), 443–488.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), 318–335.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), 370–373.Google Scholar

Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.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), 191–208. 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), 423–451. 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), 1–12. 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), 185–217.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), 337–386.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), 1–6. 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), 82–94.Google Scholar

Ratner, N., & Bruner, J. (1978). Games, social exchange and the acquisition of language. Journal of Child Language, 5(3), 391–401.Google Scholar

Reid, D. K., & Stone, C. A. (1991). Why is cognitive instruction effective? Underlying learning mechanisms. Remedial and Special Education, 12(3), 8–19.Google Scholar

Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304.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), 280–298. 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), 86–112. 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), 632–654.Google Scholar

Seixas, P. (2017). A model of historical thinking. Educational Philosophy and Theory, 49(6), 593–605. 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), 387–421.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), 349–422.Google Scholar

Stone, C. A. (1998). The metaphor of scaffolding: Its utility for the field of learning disabilities. Journal of Learning Disabilities, 31(4), 344–364.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), 1103–1127.Google Scholar

Tabak, I. (2004). Synergy: A complement to emerging patterns of distributed scaffolding. Journal of the Learning Sciences, 13(3), 305–335.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), 393–429.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. 191–200). 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), 307–355.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, 436–449. 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), 206–239. 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), 271–296. 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), 66–81.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), 477–511. 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), 1–22.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. 162–179). 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), 1783–1793.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), 89–100.Google Scholar

Wu, L., & Looi, C.-K. (2012). Agent prompts: Scaffolding for productive reflection in an intelligent learning environment. Educational Technology & Society, 15(1), 339–353.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), 261–281. doi:10.1080/02635143.2017.1386645Google Scholar

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. 275–290). 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), 149–164.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), 369–398.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. 229–270). Cambridge, MA: MIT Press.Google Scholar

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition of learning. Educational Researcher, 18(1), 32–42.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. 335–354). 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. 183–190). 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), 922–939.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), 53–67.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), 1362–1385. 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), 199–231. 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), 267–277. 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. 317–334). 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), 157–175. 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. 337–360). 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. 275–297). 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), 1270–1286. 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. 371–387). 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. 179–197). 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), 1063–1080.Google Scholar

McNeill, K. L. (2009). Teachers’ use of curriculum to support students in writing scientific arguments to explain phenomena. Science Education, 93(2), 233–268.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. 233–265). 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. 551–572). 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), 526–549. 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), 2–9.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), 232–254.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), 1–98.Google Scholar

Williams, M., & Linn, M. (2003). WISE inquiry in fifth grade biology. Research in Science Education, 32(4), 145–436.Google Scholar

Ainley, M., & Ainley, J. (2020). Motivation and learning. In Renninger, K. A. & Hidi, S. E. (Eds.), The Cambridge handbook of motivation and learning (pp. 665–688). 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. 647–672). 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), 919–998.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, 207–210.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. 102–121). 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. 254–270). 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, 125–137.Google Scholar

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.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. 388–404). 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), 350–364.Google Scholar

Boekaerts, M. (1995). Self-regulated learning: Bridging the gap between metacognitive and metamotivation theories. Educational Psychologist, 30(4), 195–200.Google Scholar

Bol, L., & Hacker, D. (2012). Calibration research: Where do we go from here? Frontiers in Psychology, 3, 1–6.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), 280–287.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), 81–96.Google Scholar

Butler, D., & Winne, P. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.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), 155–177.Google Scholar

Cromley, J., & Kunze, A. (2020). Metacognition in education: Translational research. Translational Issues in Psychological Science, 6(1), 15–20.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, 189–206.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), 127–157.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, 489–533.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), 1–26.Google Scholar

Double, K., & Birney, D. (2019). Reactivity to measures of metacognition. Frontiers in Psychology, 10, 1–12.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. 103–140). 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), 228–232.Google Scholar

Dunlosky, J., & Rawson, K. A. (2012). Overconfidence produces underachievement: Inaccurate self-evaluations undermine students’ learning and retention. Learning and Instruction, 22(4), 271–280.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), 2597–2618.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. 43–60). 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. 307–324). 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), 324–340.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), 517–529.Google Scholar

Gentner, N., & Seufert, T. (2020). The double-edged interactions of prompts and self-efficacy. Metacognition and Learning, 15, 261–289.Google Scholar

Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482.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. 2–33). 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), 18–29.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. 323–337). 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), 307–230.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. 619–646). 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. 647–677). 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), 101–121.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), 160–170.Google Scholar

Hart, J. T. (1965). Memory and the feeling-of-knowing experience. Journal of Educational Psychology, 56(4), 208–216.Google Scholar

Hart, J. T. (1967). Memory and the memory-monitoring process. Journal of Verbal Learning and Verbal Behavior, 6(5), 685–691.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, 80–90.Google Scholar

Ikeda, K., Yue, C. L., Murayama, K., & Castel, A. D. (2016). Achievement goals affect metacognitive judgments. Motivation Science, 2(4), 199–219.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), 255–278.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), 807–815.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), 157–175.Google Scholar

Kleitman, S., & Narciss, S. (2019). Introduction to the special issue “applied metacognition: Real-world applications beyond learning.” Metacognition & Learning, 14(3), 335–342.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), 349–370.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), 959–972.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), 36–69.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. 223–239). 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), 377–404.Google Scholar

Lippmann, M., Schwartz, N., Jacobson, N., & Narciss, S. (2019). The concreteness of titles affects metacognition and study motivation. Instructional Science, 47(2), 257–277.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), 116–126.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), 723–731.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), 2524–2541.Google Scholar

Metcalfe, J., & Finn, B. (2008). Evidence that judgments of learning are causally related to study choice. Psychonomic Bulletin & Review, 15(1), 174–179.Google Scholar

Metcalfe, J., & Kornell, N. (2005). A region of proximal learning model of study time allocation. Journal of Memory and Language, 52(4), 463–477.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, 223–234.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, 213–222.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), 257–260.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), 258–271.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), 281–289.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. 451–502). 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), 667–686.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, 801–813.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), 1683–1693.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), 225–250. 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), 33–45.Google Scholar

Schraw, G. (2009b). Measuring metacognitive judgements. In Hacker, D. J., Dunlosky, J., & Graesser, A. C. (Eds.), Handbook of metacognition in education (pp. 415–429). New York, NY: Routledge.Google Scholar

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475.Google Scholar

Schraw, G., Potenza, M. T., & Nebelsick-Gullet, L. (1993). Constraints on the calibration of performance. Contemporary Educational Psychology, 18(4), 455–463.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), 421–444.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), 369–380.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), 1–28.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, 177–183.Google Scholar

Weber, E. U., & Johnson, E. J. (2009). Mindful judgment and decision making. Annual Review of Psychology, 60(1), 53–85.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), 267–276.Google Scholar

Winne, P. H. (2010b). Bootstrapping learner’s self-regulated learning. Psychological Test and Assessment Modeling, 52(4), 472–490.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. 15–32). 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), 9–20.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. 36–48). New York, NY: Routledge.Google Scholar

Winne, P. H. (2020a). A proposed remedy for grievances about self-report methodologies. Frontline Learning Research, 8(3), 165–174.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), 177–193.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. 297–314). 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. 696–715). 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), 95–106.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), 954–970.Google Scholar

Zhou, M. (2013). University student’s goal profiles and metacomprehension accuracy. Educational Psychology: An International Journal of Experimental Educational Psychology, 33(1), 1–13.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. 49–64). 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. 299–315). 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

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), 270–289.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), 1–28.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. 257–291). 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. 129–160). Minneapolis, MN: University of Minnesota Press.Google Scholar

Clement, J. (1982). Students’ preconceptions in introductory mechanics. American Journal of Physics, 50(1), 66–71.Google Scholar

diSessa, A. A. (1983). Phenomenology and the evolution of intuition. In Gentner, D. & Stevens, A. (Eds.), Mental models (pp. 15–33). 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. 709–730). Oxford, England: Blackwell Publishers, Ltd.Google Scholar

diSessa, A. A. (2004). Meta-representation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.Google Scholar

diSessa, A. A. (2017). Conceptual change in a microcosm: Comparative analysis of a learning event. Human Development, 60(1), 1–37.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. 30–71). 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. 121–154). Greenwich, CT: Information Age Publishing.Google Scholar

Driver, R. (1989). Students’ conceptions and the learning of science. International Journal of Science Education, 11(5), 481–490.Google Scholar