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Teachers’ Roles, Students’ Personalities, Inquiry Learning Outcomes, and Practices of Science and Engineering:The Development and Validation of the McGill Attainment Value for Inquiry Engagement Survey in STEM Disciplines

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Abstract

Inquiry engagement is a newly defined construct that represents the participation in carrying out practices of science and engineering to achieve learning outcomes and is influenced by learners’ personalities and teachers’ roles. Expectancy value theory posits that attainment values are important components of task values that, in turn, directly influence students’ achievement-related choices and performance. The current paper developed and validated the McGill Attainment Value for Inquiry Engagement Survey (MAVIES) with undergraduate students in STEM disciplines. The MAVIES is a 67-item, learner-focused survey that addresses four components that are theoretically important for engaging in scientific inquiry: (a) teachers’ roles, (b) students’ personalities, (c) inquiry learning outcomes, and (d) practices of science and engineering. Evidence for internal consistency and construct, content, and criterion validity was obtained from 85 undergraduates who had experience with scientific inquiry in diverse STEM fields. Confirmatory factor analyses confirmed factors that were consistent with role theory, Big Five personality traits, revised Bloom’s learning outcomes, and the Next Generation Science Standards. The MAVIES instrument is a reliable and valid instrument for measuring undergraduate students’ attainment values for scientific inquiry engagement in STEM disciplines.

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References

  • Abd-El-Khalick, F., BouJaoude, S., Duschl, R. A., Lederman, N. G., Mamlok-Naaman, R., Hofstein, A., … Tuan, H.-L. (2004). Inquiry in science education: International perspectives. Science Education, 88, 397–419.

  • American Association for the Advancement of Science. (1993). Benchmarks for scientific literacy. New York, NY: Oxford University Press.

    Google Scholar 

  • Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., … Wittrock, M. C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives (complete edition) New York, NY: Longman.

  • Ashton, M. C. & Lee, K. (2001). A theoretical basis for the major dimensions of personality. European Journal of Personality, 15, 327–353. doi:10.1002/per.417.

    Article  Google Scholar 

  • Ashton, M. C. & Lee, K. (2007). Empirical, theoretical, and practical advantages of the HEXACO Model of personality structure. Personality and Social Psychology Review, 11, 150–166. doi:10.1177/1088868306294907.

    Article  Google Scholar 

  • Aulls, M. W., & Ibrahim, A. (2012). Pre-service teachers’ perceptions of effective inquiry instruction: Are effective instruction and effective inquiry instruction essentially the same? Instructional Science, 40, 119–139. doi:10.1007/s11251-010-9164-z.

  • Aulls, M. W., & Shore, B. M. (2008). Inquiry in education (Vol. 1): The conceptual foundations for research as a curricular imperative. New York, NY: Erlbaum.

  • Aulls, M. W., Kaur Magon, J., & Shore, B. M. (2015). The distinction between inquiry-based instruction and non-inquiry-based instruction in higher education: A case study of what happens as inquiry in 16 education courses in three universities. Teaching and Teacher Education, 51, 147–161. doi:10.1016/j.tate.2015.06.011.

  • Azevedo, R. (2015). Defining and measuring engagement and learning in science: conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84–94. doi:10.1080/00461520.2015.1004069.

    Article  Google Scholar 

  • Bales, R. F. & Slater, P. E. (1955). Role differentiation in small decision-making groups. In T. Parsons & R. F. Bales (Eds.), Family, socialization, and interaction processes (pp. 259–306). Glencore, IL: Free Press.

    Google Scholar 

  • Bandalos, D. L. & Finney, S. J. (2010). Factor analysis: Exploratory and confirmatory. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 93–114). New York, NY: Routledge.

    Google Scholar 

  • Barrow, L. (2006). A brief history of inquiry: From Dewey to standards. Journal of Science Teacher Education, 17, 265–278. doi:10.1007/s10972-006-9008-5.

    Article  Google Scholar 

  • Battle, A. & Wigfield, A. (2003). College women’s value orientations toward family, career, and graduate school. Journal of Vocational Behavior, 62, 56–75. doi:10.1016/S0001-8791(02)00037-4.

    Article  Google Scholar 

  • Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S. & Reiser, B. J. (2015). Epistemologies in practice: Making scientific practices meaningful for students. Journal of Research in Science Teaching. doi:10.1002/tea.21257.

    Google Scholar 

  • Bevington, P. R. & Robinson, D. K. (2003). Data reduction and error analysis for the physical sciences. Boston, MA: McGraw-Hill.

    Google Scholar 

  • Biddle, B. J. (1986). Recent developments in role theory. Annual Reviews in Sociology, 12, 67–92.

    Article  Google Scholar 

  • Biggs, J. (1987). Learning Process Questionnaire manual: Student approaches to learning and studying. Retrieved from http://www.eric.ed.gov/ERICWebPortal/Home.portal?_nfpb=true&ERICExtSearch_SearchType_0=no&_pageLabel=ERICSearchResult&ERICExtSearch_SearchValue_0=no%3Aed308199&spelling=yes.

    Google Scholar 

  • Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H. & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. New York, NY: McKay.

  • Blumenfeld, P. C., Kempler, T. M. & Krajcik, J. S. (2006). Motivation and cognitive engagement in learning environments. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 475–488). Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.

    Google Scholar 

  • Bryant, F. B. (2000). Assessing the validity of measurement. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding more multivariate statistics (1st ed., pp. 99–146). Washington, DC: American Psychological Association.

    Google Scholar 

  • Buss, D. M. (1991). Evolutionary personality psychology. Annual Review of Psychology, 42, 459–491.

    Article  Google Scholar 

  • Buss, D. M. (1996). Social adaptation and five major factors of personality. In J. S. Wiggins (Ed.), The five factor model of personality: Theoretical perspectives (pp. 180–207). New York, NY: Guilford Press.

    Google Scholar 

  • Campbell, T., Abd-Hamid, N. & Chapman, H. (2010). Development of instruments to assess teacher and student perceptions of inquiry experiences in science classrooms. Journal of Science Teacher Education, 21, 13–30. doi:10.1007/s10972-009-9151-x.

    Article  Google Scholar 

  • Chi, M. T. H. & Wylie, R. (2014). The ICAP framework: linking cognitive engagement to active learning outcomes. Educational Psychologist, 49, 219–243. doi:10.1080/00461520.2014.965823.

    Article  Google Scholar 

  • Clinton, V. (2014). The relationship between students’ preferred approaches to learning and behaviors during learning: An examination of the process stage of the 3P model. Instructional Science, 42, 817–837. doi:10.1007/s11251-013-9308-z.

    Article  Google Scholar 

  • Cole, J. S., Bergin, D. A. & Whittaker, T. A. (2008). Predicting student achievement for low stakes tests with effort and task value. Contemporary Educational Psychology, 33, 609–624. doi:10.1016/j.cedpsych.2007.10.002.

    Article  Google Scholar 

  • Collins, H. L. & Pinch, T. (1993). The golem: What everyone should know about science. Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Crawford, B. A. (2000). Embracing the essence of inquiry: New roles for science teachers. Journal of Research in Science Teaching, 37, 916–937. doi:10.1002/1098-2736(200011)37:9<916::AID-TEA4>3.0.CO;2-2.

    Article  Google Scholar 

  • Eccles, J. S. (2005). Subjective task value and the Eccles et al. model of achievement-related choices. In A. J. Elliott & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). New York, NY: Guilford Press.

    Google Scholar 

  • Eccles, J. S., Adler, T. F., Futteman, R., Goff, S., Kaczala, C. M. & Meece, J. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives (pp. 75–146). San Francisco, CA: Freeman.

    Google Scholar 

  • Eccles, J. S., O’Neill, S. A. & Wigfield, A. (2005). Ability self-perceptions and subjective task values in adolescents and children. In K. A. Moore & L. H. Lippman (Eds.), What do children need to flourish? (Vol. 3, pp. 237–249). New York, NY: Springer.

    Chapter  Google Scholar 

  • Eccles, J. S., Wigfield, A., Harold, R. D. & Blumenfeld, P. (1993). Age and gender differences in children’s self- and task perceptions during elementary School. Child Development, 64, 830–847. Retrieved from http://www.jstor.org/stable/1131221.

    Article  Google Scholar 

  • Entwistle, N. (1991). Approaches to learning and perceptions of the learning environment. Higher Education, 22, 201–204.

    Article  Google Scholar 

  • Fredricks, J. A., Blumenfeld, P. C. & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109. doi:10.3102/00346543074001059.

    Article  Google Scholar 

  • Geiser, C. (2012). Data analysis with Mplus. New York, NY: Guilford Press.

    Google Scholar 

  • Goldberg, L. R. (1993). The structure of phenotypic personality traits. American Psychologist, 48, 26–34. doi:10.1037/0003-066X.48.1.26.

    Article  Google Scholar 

  • Gott, R. & Duggan, S. (2002). Problems with the assessment of performance in practical science: which way now? Cambridge Journal of Education, 32, 183–201. doi:10.1080/03057640220147540.

    Article  Google Scholar 

  • Greeno, J. G., Collins, A. M. & Resnick, L. B. (1996). Cognition and learning. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 15–46). New York, NY: Prentice Hall.

    Google Scholar 

  • Hanauer, D. I., Hatfull, G. F. & Jacobs-Sera, D. (2009). Assessing scientific inquiry. New York, NY: Springer.

    Book  Google Scholar 

  • Hu, L.-T. & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453. doi:10.1037/1082-989x.3.4.424.

    Article  Google Scholar 

  • Jaber, L. Z., & Hammer, D. (2015). Engaging in science: A feeling for the discipline. Journal of the Learning Sciences, 1–47. doi:10.1080/10508406.2015.1088441

  • Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N:q hypothesis. Structural Equation Modeling, 10, 128–141. doi:10.1207/S15328007SEM1001_6.

    Article  Google Scholar 

  • Johnson, M. L. & Sinatra, G. M. (2013). Use of task-value instructional inductions for facilitating engagement and conceptual change. Contemporary Educational Psychology, 38, 51–63. doi:10.1016/j.cedpsych.2012.09.003.

    Article  Google Scholar 

  • Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.

    Google Scholar 

  • Knapp, T. R. & Mueller, R. O. (2010). Reliability and validity of instruments. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 337–342). New York, NY: Routledge.

    Google Scholar 

  • Komarraju, M., Karau, S. J., Schmeck, R. R. & Avdic, A. (2011). The Big Five personality traits, learning styles, and academic achievement. Personality and Individual Differences, 51, 472–477. doi:10.1016/j.paid.2011.04.019.

    Article  Google Scholar 

  • Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41, 212–218. doi:10.1207/s15430421tip4104_2.

    Article  Google Scholar 

  • Kuzel, A. J. (1999). Sampling in qualitative inquiry. In B. F. Crabtree & W. L. Miller (Eds.), Doing qualitative research (2nd ed., pp. 33–45). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Latour, B. & Woolgar, S. (1986). Laboratory life: The construction of scientific facts. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Lederman, J. S., Lederman, N. G., Bartos, S. A., Bartels, S. L., Meyer, A. A. & Schwartz, R. S. (2014). Meaningful assessment of learners’ understandings about scientific inquiry—The views about scientific inquiry (VASI) questionnaire. Journal of Research in Science Teaching, 51, 65–83. doi:10.1002/tea.21125.

    Article  Google Scholar 

  • Lombrozo, T. (2009). Explanation and categorization: How “why?” informs “what?”. Cognition, 110, 248–253. doi:10.1016/j.cognition.2008.10.007.

    Article  Google Scholar 

  • Lombrozo, T. (2010). Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions. Cognitive Psychology, 61, 303–332. doi:10.1016/j.cogpsych.2010.05.002.

    Article  Google Scholar 

  • MacCallum, R. C., Roznowski, M. & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111, 490–504. doi:10.1037/0033-2909.111.3.490.

    Article  Google Scholar 

  • MacDonald, K. (1995). Evolution, the five-factor model, and levels of personality. Journal of Personality, 63, 525–567. doi:10.1111/j.1467-6494.1995.tb00505.x.

    Article  Google Scholar 

  • MacDonald, K. (1998). Evolution, culture, and the five-factor model. Journal of Cross-Cultural Psychology, 29, 119–149. doi:10.1177/0022022198291007.

    Article  Google Scholar 

  • McCrae, R. R. (1993). Openness to experience as a basic dimension of personality. Imagination, Cognition and Personality, 13, 39–55. doi:10.2190/h8h6-qykr-keu8-gaq0.

    Article  Google Scholar 

  • McCrae, R. R. & Costa, P. T. (1996). Toward a new generation of personality theories: Theoretical contexts for the Five-Factor Model. In J. S. Wiggins (Ed.), The Five Factor Model of personality: Theoretical perspectives (pp. 51–87). New York, NY: Guilford Press.

    Google Scholar 

  • McCrae, R. R. & Costa, P. T. (1997). Conceptions and correlates of openness to experience. In R. Hogan, J. A. Johnson & S. Briggs (Eds.), Handbook of personality psychology (pp. 825–847). San Diego, CA: Academic.

    Chapter  Google Scholar 

  • Miles, M. B. & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Millar, R. & Driver, R. (1987). Beyond processes. Studies in Science Education, 14, 33–62. doi:10.1080/03057268708559938.

    Article  Google Scholar 

  • Mody, C. C. M. (2015). Scientific practice and science education. Science Education, 99, 1026–1032. doi:10.1002/sce.21190.

    Article  Google Scholar 

  • Moeed, A. (2013). Science investigation that best supports student learning: teachers’ understanding of science investigation. International Journal of Environmental and Science Education, 8, 537–559.

    Article  Google Scholar 

  • Mueller, R. O. & Hancock, G. R. (2010). Structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 371–384). New York, NY: Routledge.

    Google Scholar 

  • National Research Council. (1996). National Science Education Standards. Washington, DC: National Academies Press.

    Google Scholar 

  • National Research Council. (2000). Inquiry and the National Science Education Standards: A guide for teaching and learning. Washington, DC: National Academies Press.

    Google Scholar 

  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press.

    Google Scholar 

  • Nettle, D. (2006). The evolution of personality variation in humans and other animals. American Psychologist, 61, 622–631. doi:10.1037/0003-066X.61.6.622.

    Article  Google Scholar 

  • Next Generation Science Standards Lead States. (2013). Next Generation Science Standards: For states, by states (Vol. 1). Washington, DC: National Academies Press.

  • Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage.

    Google Scholar 

  • Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., …. Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47–61. doi:10.1016/j.edurev.2015.02.003

  • Peterson, C. A. (2012). Mentored engagement of secondary science students, plant scientists, and teachers in an inquiry-based online learning environment. (Unpublished doctoral dissertation), Texas A&M University, College Station, TX.

  • Pett, M. A., Lackey, N. R. & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage.

    Book  Google Scholar 

  • Pickering, A. (1995). The mangle of practice: Time, agency, and science. Chicago, IL: University of Chicago Press.

    Book  Google Scholar 

  • Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135, 322–338. doi:10.1037/a0014996.

    Article  Google Scholar 

  • Ramsden, P. (2003). Approaches to learning. In P. Ramsden (Ed.), Learning to teach in higher education (2nd ed., pp. 39–61). London, England: Routledge.

    Google Scholar 

  • Redden, K. C., Simon, R. A., & Aulls, M. W. (2007). Alignment in constructivist-oriented teacher education: Identifying pre-service teacher characteristics and associated learning outcomes. Teacher Education Quarterly, 34, 149–164. Retrieved from http://www.jstor.org/stable/23478999.

  • Roberts, R. & Gott, R. (2003). Assessment of biology investigations. Journal of Biological Education, 37, 114–121. doi:10.1080/00219266.2003.9655865.

    Article  Google Scholar 

  • Roberts, R. & Gott, R. (2004). A written test for procedural understanding: a way forward for assessment in the UK science curriculum? Research in Science & Technological Education, 22, 5–21. doi:10.1080/0263514042000187511.

    Article  Google Scholar 

  • Roberts, R. & Gott, R. (2006). Assessment of performance in practical science and pupil attributes. Assessment in Education: Principles, Policy & Practice, 13, 45–67. doi:10.1080/09695940600563652.

    Article  Google Scholar 

  • Rudolph, J. L. (2005). Epistemology for the masses: the origins of “the scientific method” in American schools. History of Education Quarterly, 45, 341–376. doi:10.1111/j.1748-5959.2005.tb00039.x.

    Article  Google Scholar 

  • Saunders-Stewart, K. S., Gyles, P. D. T., Shore, B. M., & Bracewell, R. J. (2012). Student outcomes in inquiry: Students’ perspectives. Learning Environments Research, 18, 289-311. doi:10.1007/s10984-015-9185-2.

  • Schermelleh-Engel, K., Moosbrugger, H. & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8, 23–74. Retrieved from http://www.dgps.de/fachgruppen/methoden/mpr-online/issue20/art2/mpr130_13.pdf.

    Google Scholar 

  • Shavelson, R. J., Solano-Flores, G. & Ruiz-Primo, M. A. (1998). Toward a science performance assessment technology. Evaluation and Program Planning, 21, 171–184. doi:10.1016/S0149-7189(98)00005-6.

    Article  Google Scholar 

  • Shore, B. M., Birlean, C., Walker, C. L., Ritchie, K. C., LaBanca, F., & Aulls, M. W. (2009). Inquiry literacy: A proposal for a neologism. Learning Landscapes, 3, 139–155. Retrieved from http://www.learninglandscapes.ca/.

  • Shore, B. M., Chichekian, T., Syer, C. A., Aulls, M. W., &Frederiksen, C. H. (2012). Planning, enactment, and reflection in inquiry-based learning: Validating the McGill Strategic Demands of Inquiry Questionnaire. International Journal of Science and Mathematics Education, 10, 315–337. doi:10.1007/s10763-011-9301-4.

  • Simonton, D. K. (2004). Creativity in science: Chance, logic, genius, and Zeitgeist. Cambridge, England: Cambridge University Press.

    Book  Google Scholar 

  • Slater, P. E. (1955). Role differentiation in small groups. American Sociological Review, 20, 300–310. Retrieved from http://www.jstor.org/stable/2087389.

    Article  Google Scholar 

  • Spronken-Smith, R. (2010). Undergraduate research and inquiry-based learning: Is there a difference? Insights from research in New Zealand. CUR (Council on Undergraduate Research) Quarterly, 30, 28–35. Retrieved from http://www.cur.org/assets/1/7/Spronken-Smith.pdf.

    Google Scholar 

  • Spronken-Smith, R. & Walker, R. (2010). Can inquiry-based learning strengthen the links between teaching and disciplinary research? Studies in Higher Education, 35, 723–740. doi:10.1080/03075070903315502.

    Article  Google Scholar 

  • Spronken-Smith, R., Walker, R., Batchelor, J., O’Steen, B. & Angelo, T. (2012). Evaluating student perceptions of learning processes and intended learning outcomes under inquiry approaches. Assessment and Evaluation in Higher Education, 37, 57–72. doi:10.1080/02602938.2010.496531.

    Article  Google Scholar 

  • Stroupe, D. (2015). Describing “science practice” in learning settings. Science Education, 99, 1033–1040. doi:10.1002/sce.21191.

    Article  Google Scholar 

  • Talanquer, V., Tomanek, D. & Novodvorsky, I. (2013). Assessing students’ understanding of inquiry: What do prospective science teachers notice? Journal of Research in Science Teaching, 50, 189–208. doi:10.1002/tea.21074.

    Article  Google Scholar 

  • Tang, X., Coffey, J. E., Elby, A. & Levin, D. M. (2010). The scientific method and scientific inquiry: tensions in teaching and learning. Science Education, 94, 29–47. doi:10.1002/sce.20366.

    Google Scholar 

  • Tudor, I. (1993). Teacher roles in the learner-centred classroom. ELT Journal, 47, 22–31.

    Article  Google Scholar 

  • Turner, R. H. (2001). Role theory. In J. H. Turner (Ed.), Handbook of sociological theory (pp. 233–254). New York, NY: Kluwer. Retrieved from http://www.social-sciences-and-humanities.com/pdf/Handbook-of-Sociological-Theory.pdf.

    Google Scholar 

  • van Lieshout, C. F. M. (2000). Lifespan personality development: Self-organising goal-oriented agents and developmental outcome. International Journal of Behavioral Development, 24, 276–288. doi:10.1080/01650250050118259.

    Article  Google Scholar 

  • Walker, C. L., & Shore, B. M. (2015). Understanding classroom roles in inquiry education: Linking role theory and social constructivism to the concept of role diversification. SAGE Open, 5, 1–13. doi:10.1177/2158244015607584.

  • Wenning, C. J. (2007). Assessing inquiry skills as a component of scientific literacy. Journal of Physics Teacher Education Online, 4, 21–24.

    Google Scholar 

  • West, S. G., Taylor, A. B. & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). New York, NY: Guilford Press.

    Google Scholar 

  • Wigfield, A. & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35. doi:10.1016/j.dr.2009.12.001.

    Article  Google Scholar 

  • Wigfield, A. & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. doi:10.1006/ceps.1999.1015.

    Article  Google Scholar 

  • Wigfield, A., Tonks, S. & Klauda, S. L. (2009). Expectancy-value theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 55–75). New York, NY: Routledge.

    Google Scholar 

  • Wilson, M. & Sloane, K. (2000). From principles to practice: An embedded assessment system. Applied Measurement in Education, 13, 181–208. doi:10.1207/S15324818AME1302_4.

    Article  Google Scholar 

  • Windschitl, M. (2004). Folk theories of “inquiry:” how preservice teachers reproduce the discourse and practices of an atheoretical scientific method. Journal of Research in Science Teaching, 41, 481–512. doi:10.1002/tea.20010.

    Article  Google Scholar 

  • Woolgar, S. & Lezaun, J. (2013). The wrong bin bag: A turn to ontology in science and technology studies? Social Studies of Science, 43, 321–340. doi:10.1177/0306312713488820.

    Article  Google Scholar 

  • Zachos, P. (2005). Pendulum phenomena and the assessment of scientific inquiry capabilities. In M. R. Matthews, C. F. Gauld & A. Stinner (Eds.), The pendulum (pp. 349–362). Dordrecht, The Netherlands: Springer.

    Google Scholar 

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  1. Department of Educational and Counselling Psychology, McGill University, Montreal, Quebec, Canada

    Ahmed Ibrahim, Mark W. Aulls & Bruce M. Shore

  2. Department of Pyschology, University of California, Riverside, USA

    Ahmed Ibrahim

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Ibrahim, A., Aulls, M.W. & Shore, B.M. Teachers’ Roles, Students’ Personalities, Inquiry Learning Outcomes, and Practices of Science and Engineering:The Development and Validation of the McGill Attainment Value for Inquiry Engagement Survey in STEM Disciplines. Int J of Sci and Math Educ 15, 1195–1215 (2017). https://doi.org/10.1007/s10763-016-9733-y

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