Deeper learning as integrated knowledge and fascination for Science

References

• American Institutes for Research. (2014). Does deeper learning improve student outcomes? Washington: American Institutes for Research.
   Google Scholar
• Ark, V. D., & Schneider, C. (2014). Deeper learning for every student every day. GettingSmart. https://hewlett.org/wp-content/uploads/2016/08/Deeper.
   Google Scholar
• Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
   Google Scholar
• Brislin, R. W. (1980). Translation and content analysis of oral and written material. In H. C. Triandis, & J. W. Berry (Eds.), Handbook of cross-cultural psychology (pp. 389–444). Boston: Allyn and Bacon.
   Google Scholar
• Brügger, A., Kaiser, F. G., & Roczen, N. (2011). One for all? Connectedness to nature, inclusion of nature, environmental identity, and implicit association with nature. European Psychologist, 16, 324–333. doi: 10.1027/1016-9040/a000032
   Web of Science ®Google Scholar
• Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105. doi: 10.1037/h0046016
   PubMed Web of Science ®Google Scholar
• Davier, M. V., & Davier, A. A. V. (2007). A unified approach to irt scale linking and scale transformations. Methodology, 3(3), 115–124. doi: 10.1027/1614-2241.3.3.115
   Google Scholar
• Dede, C. (2014). The role of digital technologies in deeper learning. Students at the center: Deeper learning research series. Harvard: Harvard University.
   Google Scholar
• DeFleur, M. L., & Westie, F. R. (1958). Verbal attitudes and overt acts: An experiment on the salience of attitudes. American Sociological Review, 23, 667–673. doi: 10.2307/2089055
   Web of Science ®Google Scholar
• DeWitt, J., Archer, L., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2011). High aspirations but low progression: The science aspirations–careers paradox amongst minority ethnic students. International Journal of Science and Mathematics Education, 9, 243–271. doi: 10.1007/s10763-010-9245-0
   Web of Science ®Google Scholar
• Dummer, T. J. B., Cook, I. G., Parker, S. L., Barrett, G. A., & Hull, A. P. (2008). Promoting and assessing ‘deep learning’ in geography fieldwork: An evaluation of reflective field diaries. Journal of Geography in Higher Education, 32(3), 459–479. doi: 10.1080/03098260701728484
   Web of Science ®Google Scholar
• Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich College.
   Google Scholar
• Fullan, M., & Langworthy, M. (2014). A rich seam. How new pedagogies find deep learning. London: Pearson.
   Google Scholar
• Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48, 1159–1176. doi: 10.1002/tea.20442
   Web of Science ®Google Scholar
• Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25, 199–237. doi: 10.1080/08993408.2015.1033142
   Web of Science ®Google Scholar
• Hambleton, R. K. (1996). Guidelines for adapting educational and psychological tests. European Journal of Psychological Assessment, 10, 229–244.
   Google Scholar
• Hilton, M. (2008). Research on future skill demands: A workshop summary. Washington, DC: National Academies Press.
   Google Scholar
• Kaiser, F. G., & Byrka, K. (2015). The Campbell paradigm as a conceptual alternative to the expectation of hypocrisy in contemporary attitude research. The Journal of Social Psychology, 155, 12–29. doi: 10.1080/00224545.2014.959884
   PubMed Web of Science ®Google Scholar
• Kaiser, F. G., Byrka, K., & Hartig, T. (2010). Reviving Campbell's paradigm for attitude research. Personality and Social Psychology Review, 14, 351–367. doi: 10.1177/1088868310366452
   PubMed Web of Science ®Google Scholar
• Kaiser, F. G., & Wilson, M. (2019). The Campbell paradigm as a behavior-predictive reinterpretation of the classical tripartite model of attitudes. European Psychologist, 24, 359–374. doi: 10.1027/1016-9040/a000364
   PubMed Web of Science ®Google Scholar
• Kolen, M. J., & Brennan, R. L. (2014). Test equating, scaling, and linking: Methods and practices (3rd ed.). New York: Springer.
   Google Scholar
• Martin, M. O., Mullis, I. V. S., Foy, P., & Hooper, M. (2016). TIMSS 2015 international results in science. Fourth grade in science. Boston: International Association for the Evaluation of Educational Achievement.
   Google Scholar
• Masters, G. N. (1982). A rasch model for partial credit scoring. Psychometrika, 47, 149–174. doi: 10.1007/BF02296272
   Web of Science ®Google Scholar
• Meijer, R. R. (1994). The number of guttman errors as a simple and powerful person-fit statistic. Applied Psychological Measurement, 18, 311–314. doi: 10.1177/014662169401800402
   Web of Science ®Google Scholar
• Moore, R. W., & Foy, R. L. H. (1997). The scientific attitude inventory: A revision(SAI II). Journal of Research in Science Teaching, 34, 327–336. doi: 10.1002/(SICI)1098-2736(199704)34:4<327::AID-TEA3>3.0.CO;2-T
   Web of Science ®Google Scholar
• OECD. (2012). Pisa 2012 assessment and analytical framework. Mathematics, reading, science, problem solving and financial literacy. Paris: OECD Publishing.
   Google Scholar
• Otto, S., Kröhne, U., & Richter, D. (2018). The dominance of introspective measures and what this implies: The example of environmental attitude. PLOS ONE, 13(2), e0192907. doi: 10.1371/journal.pone.0192907
   PubMed Web of Science ®Google Scholar
• Pellegrino, J. W., & Hilton, M. L. (2013). Education for life and work: Developing transferable knowledge and skills in the 21st century. Washington, DC: National Academies Press.
   Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. doi: 10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Chicago, IL: University of Chicago.
   Google Scholar
• Robitzsch, A., Kiefer, T., & Wu, M. (Producer). (2018). TAM: Test analysis modules. R package version 2.12-18. Retrieved from https://CRAN.R-project.org/package=TAM
   Google Scholar
• Roczen, N., Kaiser, F. G., Bogner, F. X., & Wilson, M. (2014). A Competence Model for Environmental Education. Environment and Behavior, 46, 972–992. doi: 10.1177/0013916513492416
   Web of Science ®Google Scholar
• Rosenberg, M. J., & Hovland, C. I. (1960). Cognitive, affective, and behavioral components of attitudes. In M. J. Rosenberg & C. I. Hovland (Eds.), Attitude organization and change: An analysis of consistency among attitude components (pp. 1–14). New Haven, CT: Yale University.
   Google Scholar
• Schumm, M. F., & Bogner, F. X. (2016). Measuring adolescent science motivation. International Journal of Science Education, 38, 434–449. doi: 10.1080/09500693.2016.1147659
   Web of Science ®Google Scholar
• Sjøberg, S., & Schreiner, C. (2010). The rose project. An overview and key findings. Oslo: University of Oslo.
   Google Scholar
• Wilson, M. (2013). Using the concept of a measurement system to characterize measurement models used in psychometrics. Measurement, 46, 3766–3774. doi: 10.1016/j.measurement.2013.04.005
   Web of Science ®Google Scholar
• Wu, M., & Adams, R. J. (2013). Properties of Rasch residual fit statistics. Journal of Applied Measurement, 14, 339–355.
   PubMedGoogle Scholar
• Zeiser, K. L., Taylor, J., Rickles, J., Garet, M. S., & Segeritz, M. (2014). Evidence of deeper learning outcomes. New York: American Institutes for Research.
   Google Scholar