SEARCH

SEARCH BY CITATION

REFERENCES

  • American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York, NY: Oxford University Press.
  • Assaraf, O. B., & Orion, N. (2005). Development of systems thinking skills in the context of earth system education. Journal of Research in Science Teaching, 42, 518560.
  • Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition-implications for the design of computer-based scaffolds. Instructional Science, 33, 367379.
  • Bell, B., & Cowie, B. (2001). Formative assessment and science education. Dordrecht, The Netherlands: Kluwer Academic Publishers.
  • Bennett, R. E. (2010). Cognitively Based Assessment of, for, and as Learning (CBAL): A preliminary theory of action for summative and formative assessment. Measurement, 2–3, 7091.
  • Bennett, R. E., & Gitomer, D. H. (2009). Transforming K–12 assessment: Integrating accountability testing, formative assessment and professional support. In C. Wyatt-Smith & J. Cumming (Eds.), Educational assessment in the 21st century (pp. 4361). New York, NY: Springer.
  • Bennett, R. E., Persky, H., Weiss, A. R., & Jenkins, F. (2007). Problem solving in technology-rich environments: A report from the NAEP technology-based assessment project (NCES 2007–466). Washington, DC: National Center for Education Statistics.
  • Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientific argumentation: Understanding student work and designing supportive instructional contexts. Science Education, 94, 765793.
  • Berland, L. K., & Reiser, B. J. (2008). Making sense of argumentation and explanation. Science Education, 93, 2655.
  • Black, P., & Wiliam, D. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kaplan, 80, 139148.
  • Braaten, M., & Windschitl, M. (2011). Working toward a stronger conceptualization of scientific explanation for science education. Science Education, 95, 639669.
  • Bransford, J., Brown, A., & Cocking, D. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
  • Brophy, J. E. (1988). On motivating students. In D. Berliner & B. Rosenshine (Eds.), Talks to teachers (pp. 201245). New York, NY: Random House.
  • Brown, N. J., & Wilson, M. (2011). A model of cognition: The missing cornerstone of assessment. Educational Psychology Review, 2, 221234.
  • Buehl, M. M., & Alexander, P. A. (2001). Beliefs about academic knowledge. Educational Psychology Review, 13, 353382.
  • Carey, S., & Smith, C. L. (1993). On understanding the nature of scientific knowledge. Educational Psychologist, 28, 235251.
  • Chan, K., & Elliott, R. G. (2004). Relational analysis of personal epistemology and conceptions about teaching and learning. Teaching and Teacher Education, 20, 817831.
  • Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology: Educational design and cognitive science (Vol. 5, pp. 161238). Mahwah, NJ: Erlbaum.
  • Chi, M. T. H., & Bassok, M. (1989). Learning from examples via self-explanations. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 251282). Hillsdale, NJ: Erlbaum.
  • Corcoran, T., Mosher, F., & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform. Philadelphia, PA: Consortium for Policy Research in Education.
  • Cuccio-Schirripa, S., & Steiner, H. E. (2000). Enhancement and analysis of science question level for middle school students. Journal of Research in Science Teaching, 37, 210224.
  • Davidson, J. E., & Sternberg, J. R. (1998). Smart problem solving: How metacognition helps. In J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 4768). Mahwah, NJ: Erlbaum.
  • De Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179201.
  • Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative-learning: Cognitive and computational approaches (pp. 119). Oxford, England: Elsevier.
  • Driver, R., Leach, J., Millar, R., & Scott, P. (1996). Young people's images of science. Philadelphia, PA: Open University Press.
  • Duell, O. K., & Schommer-Aikins, M. (2001). Measures of people's beliefs about knowledge and learning. Educational Psychology Review, 13, 419449.
  • Duit, R., & Treagust, D. F. (2003). Conceptual change: A powerful framework for improving science teaching and learning. International Journal of Science Education, 25, 671688.
  • Dunbar, K., & Klahr, D. (1988). Developmental differences in scientific discovery strategies. In D. Klahr & K. Kotovsky (Eds.), Complex information processing: The impact of Herbert A. Simon (pp. 109144). Hillsdale, NJ: Erlbaum.
  • Duschl, R. A. (2003). Assessment of inquiry. In J. M. Atkin & J. E. Coffey (Eds.), Everyday assessment (pp. 4160). Arlington, VA: National Science Teachers Association Press.
  • Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse. Studies in Science Education, 38, 3972.
  • Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academies Press.
  • Erduran, S., Simon, S., & Osborne, J. (2004). TAPping into argumentation: Developments in the application of Toulmin's argument pattern for studying science discourse. Science Education, 88, 915933.
  • Flavell, H. (1979). Metacognition and cognitive monitoring: A new era of cognitive developmental inquiry. The American Psychologist, 34, 906911.
  • Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: The MIT Press.
  • Furtak, E. M. (2012). Linking a learning progression for natural selection to teachers' enactment of formative assessment. Journal of Research in Science Teaching, 49, 11811210.
  • Gilbert, J. (1995, April). The role of models and modelling in some narratives in science learning. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
  • Gobert, J. D., & Buckley, B. (2000). Special issue editorial: Introduction to model-based teaching and learning. International Journal of Science Education, 22, 891894.
  • Gobert, J. D., & Pallant, A. (2004). Fostering students' epistemologies of models via authentic model-based tasks. Journal of Science Education and Technology, 13, 722.
  • Golan, R., & Reiser, B. J. (2002). Investigating students' reasoning about the complexity manifested in molecular genetics phenomena. In P. Bell & S. Reed (Eds.), Proceedings of the 5th international conference for the learning sciences. Keeping learning complex: Fostering multidisciplinary research efforts (pp. 237244). Mahwah, NJ: Erlbaum.
  • Gotwals, A. W., & Songer, N. B. (2006). Measuring students' scientific content and inquiry reasoning. In S. Barab, K. Hay, & D. Hickey (Eds.), Proceedings of the 7th international conference of the learning sciences (pp. 196202). Mahwah, NJ: Erlbaum.
  • Hammer, D. (1994). Epistemological beliefs in introductory physics. Cognition and Instruction, 12, 151183.
  • Harrison, A. G., & Treagust, D. F. (2000). A typology of school science models. International Journal of Science Education, 22, 10111026.
  • Hmelo, C. E., & Lin, X. (2000). Becoming self-directed learners: Strategy development in problem-based learning. Mahwah, NJ: Erlbaum.
  • Hofer, B. K. (2000). Dimensionality and disciplinary differences in personal epistemology. Contemporary Educational Psychology, 25, 378405.
  • Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67, 88140.
  • Hogan, K. (2000). Exploring a process view of students' knowledge about the nature of science. Science Education, 84, 5170.
  • Jiménez-Aleixandre, M. P., & Pereiro-Munhoz, C. (2002). Knowledge producers or knowledge consumers? Argumentation and decision making about environmental management. International Journal of Science Education, 24, 11711190.
  • Johnson, D. W., & Johnson, R. (1992). Positive interdependence: Key to effective cooperation. In R. Hertz-Lazarowitz & N. Miller (Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp. 174199). Cambridge, England: Cambridge University Press.
  • Johnson, P. (1998). Progression in children's understanding of a ‘basic’ particle theory: A longitudinal study. International Journal of Science Education, 20, 393412.
  • Johnson, P. (2000). Children's understanding of substances, Part 1: Recognizing chemical change. International Journal of Science Education, 22, 719737.
  • Johnson, P. (2002). Children's understanding of substances, Part 2: Explaining chemical change. International Journal of Science Education, 24, 10371054.
  • Jones, A., & Issroff, K. (2004). Learning technologies: Affective and social issues in computer-supported collaborative learning. Computers & Education, 44, 395408.
  • Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press.
  • Krajcik, J., McNeill, K. L., & Reiser, B. J. (2008). Learning-goals-driven design model: Developing curriculum materials that align with national standards and incorporate project-based pedagogy. Science Education, 92, 13.
  • Krajcik, J., & Reiser, B. J. (2004). IQWST: Investigating and questioning our world through science and technology. Ann Arbor, MI: University of Michigan.
  • Krnel, D., Watson, R., & Glazar, S. (1998). Survey of research related to the development of the concept of ‘matter.’ International Journal of Science Education, 20, 257289.
  • Kuhn, D., Amsel, E., O'Loughlin, M., Schauble, L., Leadbeater, B., & Yotive, W. (1988). The development of scientific thinking skills. Orlando, FL: Academic Press.
  • Lehrer, R., & Schauble, L. (2000). Inventing data structures for representational science: Co-constituting inscription and thought. In J. Byrnes & E. D. Amsel (Eds.), Language, literacy, and cognitive development: The development and consequences of symbolic communication (pp. 3974). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Lehrer, R., & Schauble, L. (2012). Seeding evolutionary thinking by engaging children in modeling its foundations. Science Education, 96, 701724.
  • Linn, M. C., & Songer, N. B. (1993). How do students make sense of science? Merrill-Palmer Quarterly, 39, 4773.
  • Liu, L., & Hmelo-Silver, C. E. (2009). Promoting complex systems learning through the use of conceptual representations in hypermedia. Journal of Research in Science Teaching, 46, 10231040.
  • Liu, L., & Hmelo-Silver, C. E. (2010). Computer-supported collaborative scientific conceptual change: Effects of collaborative processes on student learning. In B. Ertl (Ed.), E-collaborative knowledge construction: Learning from computer-supported and virtual environments (pp. 124138). Hershey, PA: IGI Global Publication.
  • Liu, X., & Lesniak, K. M. (2005). Students' progression of understanding the matter concept from elementary to high school. Science Education, 89, 433450.
  • Lombrozo, T. (2006). The structure and function of explanations. Trends in Cognitive Sciences, 10, 464470.
  • McNeill, K. L., & Krajcik, J. (2008). Inquiry and scientific explanations: Helping students use evidence and reasoning. In J. Luft, R. Bell, & J. Gess-Newsome (Eds.), Science as inquiry in the secondary setting (pp. 121134). Arlington, VA: National Science Teachers Association Press.
  • McNeill, K. L., Lizotte, D. J., Krajcik, J., & Marx, R. W. (2006). Supporting students' construction of scientific explanations by fading scaffolds in instructional materials. Journal of the Learning Sciences, 15, 153191.
  • Merritt, J. (2010). Tracking students' understanding of the particle nature of matter. Ann Arbor, MI: University of Michigan.
  • Michaels, S., Shouse, A. W., & Schweingruber, H. A. (2008). Ready, set, science. Washington, DC: National Academies Press.
  • Mohan, L., Chen, J., & Anderson, C. W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching, 46, 675698.
  • Nakhleh, M. B., & Samarapungavan, A. (1999). Elementary school children's beliefs about matter. Journal of Research in Science Teaching, 36, 777805.
  • National Assessment Governing Board. (2009). Science framework for the 2011 national assessment for educational progress. Washington, DC: Author.
  • National Research Council. (1996). National science education standards: Observe, interact, change, learn. Washington, DC: National Academy Press.
  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academy Press.
  • National Science Board Commission on Precollege Education in Mathematics Science and Technology. (1983). Educating Americans for the 21st century: A plan of action for improving mathematics, science, and technology education for all American elementary and secondary students so that their achievement is the best in the world by 1995. Washington, DC: National Science Foundation.
  • Next Generation Science Standards. (2013). Retrieved from http://www.nextgenscience.org/next-generation-science-standards.
  • Ofer, G., & Durban, J. (1999). Curiosity: Reflections on its nature and functions. American Journal of Psychotherapy, 53, 3551.
  • Organisation for Economic Co-operation and Development. (2004). Learning for tomorrow's world: First results from PISA 2003. Paris, France: OECD Publishing.
  • Osborne, J. (2007). Science education for the twenty first century. Eurasia Journal of Mathematics, Science & Technology Education, 3, 173184.
  • Osborne, J., Erduran, S., & Simon, S. (2004). Enhancing the quality of argument in school science. Journal of Research in Science Teaching, 41, 9941020.
  • Osborne, J., Erduran, S., Simon, S., & Monk, M. (2001). Enhancing the quality of argument in school science. School Science Review, 82, 6370.
  • Osborne, J., & Patterson, A. (2011). Scientific argument and explanation: A necessary distinction? Science Education, 95, 627638.
  • Papageorgiou, G., & Johnson, P. (2005). Do particle ideas help or hinder pupils' understanding of phenomena? International Journal of Science Education, 27, 12991317.
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books.
  • Partnership for 21st Century Skills. (2007). The intellectual and policy foundations of the 21st century skills framework. Tucson, AZ: Author.
  • Pea, R. D. (1993). Learning scientific concepts through material and social activities: Conversational analysis meets conceptual change. Educational Psychology Review, 28, 265277.
  • Pellegrino, J., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press.
  • Penner, D. E., Giles, N. D., Lehrer, R., & Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 2, 125144.
  • Pintrich, P. R., Smith, D., Garcia, T., & McKeachie, W. (1993). Predictive validity and reliability of the motivated strategies for learning questionnaire (MSLQ). Educational Psychological Measurement, 53, 801813.
  • Quellmalz, E. S., & Pellegrino, J. W. (2009). Technology and testing. Science Education, 323, 7579.
  • Quellmalz, E. S., Timms, M. J., Silberglitt, M. D., & Buckley, B. C. (2012). Science assessments for all: Integrating science simulations into balanced state science assessment systems. Journal of Research in Science Teaching, 49, 363393.
  • Rogat, A., Anderson, C., Foster, J., Goldberg, F., Hicks, J., Kanter, D., … Wiser, M. (2011). Developing learning progressions in support of the new science standards: A RAPID workshop series. Retrieved from http://www.cpre.org/developing-learning-progressions-support-new-science-standards-rapid-workshop-series.
  • Roschelle, J. (1992). Learning by collaboration: Convergent conceptual change. Journal of the Learning Sciences, 2, 235276.
  • Sabelli, N. H. (2006). Complexity, technology, science, and education. Journal of the Learning Sciences, 15, 59.
  • Saye, J., & Brush, T. (2001). The use of embedded scaffolds with hypermedia-supported student-centered learning. Journal of Educational Multimedia and Hypermedia, 10, 333356.
  • Scardamalia, M., & Bereiter, C. (2006). Knowledge building. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97115). New York, NY: Cambridge University Press.
  • Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Acher, A., Fortus, D., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46, 632654.
  • Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction, 23, 165205.
  • Shin, N., Stevens, S. Y., & Krajcik, J. (2010). Using construct-centered design as a systematic approach for tracking student learning over time. London, England: Routledge, Taylor & Francis Group.
  • Slotta, J. D. (2004). The web-based inquiry science environment (WISE): Scaffolding knowledge integration in the science classroom. In M. C. Linn, P. Bell, & E. Davis (Eds.), Internet environments for science education (pp. 203232). Mahwah, NJ: Lawrence Erlbaum.
  • Smith, C. L., Maclin, D., Houghton, C., & Hennessey, M. G. (2000). Sixth-grade students' epistemologies of science: The impact of school science experiences on epistemological development. Cognition and Instruction, 18, 349422.
  • 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, 1–2, 198.
  • Snow, R. E., & Lohman, D. F. (1989). Implications of cognitive psychology for educational measurement. In R. Linn (Ed.), Educational measurement (3rd ed., pp. 262331). New York, NY: American Council on Education/Collier Macmillan.
  • Songer, N. B., Kelcey, B., & Gotwals, A. W. (2009). When and how does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46, 610631.
  • Stathopoulou, C., & Vosniadou, S. (2006). Exploring the relationship between physics-related epistemological beliefs and physics understanding. Contemporary Educational Psychology, 32, 255281.
  • Stavy, R. (1990). Children's conception of changes in the state of matter: From liquid (or solid) to gas. Journal of Research in Science Teaching, 27, 247266.
  • Stavy, R. (1991). Children's ideas about matter. School Science and Curriculum, 91, 240244.
  • Sterman, J. (2000). Business dynamics: Systems thinking and modeling for a complex world. New York, NY: McGraw-Hill.
  • Stevens, S. Y., Delgado, C., & Krajcik, J. (2010). Developing a hypothetical multi-dimensional learning progression for the nature of matter. Journal of Research in Science Teaching, 47, 687715.
  • Stevens, T., Olivárez, J. A., & Hamman, D. (2006). The role of cognition, motivation, and emotion in explaining the mathematics achievement gap between Hispanic and White students. Hispanic Journal of Behavioral Sciences, 28, 161186.
  • Stipek, D., Salmon, J., Givvin, K., Kazemi, E., Saxe, G., & MacGyvers, V. (1998). The value (and convergence) of practices suggested by motivation research and promoted by mathematics education reformers. Journal for Research in Mathematics Education, 29, 465488.
  • Strevens, M. (2006). Scientific explanation. In D. M. Borchert (Ed.), Encyclopedia of - philosophy (pp. 531556). Detroit, MI: Macmillan.
  • Suthers, D. D. (2006). Technology affordances for intersubjective meaning making. International Journal of Computer Supported Collaborative Learning, 1, 315337.
  • Toulmin, S. (1958). The uses of argument. Cambridge, England: Cambridge University Press.
  • Trout, J. D. (2007). The psychology of scientific explanation. Philosophy Compass, 2, 564591.
  • Vygotsky, L. S. (1978). Interaction between learning and development. In M. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Mind in society: The development of higher psychological processes (pp. 7991). Cambridge, MA: Harvard University Press.
  • Walton, D. N. (1989). Informal logic. Cambridge, England: Cambridge University Press.
  • Weinstein, C., Zimmermann, S., & Palmer, D. (1988). Assessing learning strategies: The design and development of the LASSI. In C. Weinstein, E. Goetz, & P. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction, and evaluation (pp. 2540). San Diego, CA: Academic Press.
  • White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10, 1100.
  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition and Instruction, 24, 171209.
  • Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8, 319.
  • Wiser, M., & Smith, C. L. (2008). Learning and teaching about matter in grades K-8: When should the atomic-molecular theory be introduced? In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 205239). New York, NY: Routledge.
  • Wiser, M., Smith, C. L., & Doubler, S. (2012). Learning progressions as tool for curriculum development. In A. C. Alonzo & A. W. Gotwals (Eds.), Learning progression in science (pp. 359403). New York, NY: Sense Publishers.
  • Yarden, A., Brill, G., & Falk, H. (2001). Primary literature as a basis for a high-school biology curriculum. Journal of Biology Education, 35, 190195.
  • Yoon, S. A., Liu, L., & Goh, S. (2009). Exploring the process of convergent adaptation in technology-based science curriculum construction. In Proceedings of the 9th international conference on computer supported collaborative learning (pp. 272281). Rhodes, Greece: International Society of the Learning Sciences.
  • Zacharia, Z. C. (2007). Comparing and combining real and virtual experimentation: An effort to enhance students' conceptual understanding of electric circuits. Journal of Computer Assisted Learning, 23, 120132.
  • Zhu, Y., & Leung, F. K. S. (2011). Motivation and achievement: Is there an East Asian model? International Journal of Science and Mathematics Education, 9, 11891212.