Instructional Science

, Volume 44, Issue 2, pp 125–145 | Cite as

A multivariate model of conceptual change

  • Gita Taasoobshirazi
  • Benjamin Heddy
  • MarLynn Bailey
  • John Farley
Article

Abstract

The present study used the Cognitive Reconstruction of Knowledge Model (CRKM) model of conceptual change as a framework for developing and testing how key cognitive, motivational, and emotional variables are linked to conceptual change in physics. This study extends an earlier study developed by Taasoobshirazi and Sinatra (J Res Sci Teach 48:901–918, 2011) by providing a more comprehensive test of the CRKM. The variables included in the model tested in this study included emotions: boredom, enjoyment and anxiety; approach goals, need for cognition, motivation, deep cognitive engagement, course grade, and conceptual change. Results of a path analysis conducted on 117 introductory-level college physics students indicated that enjoyment was linked to students’ motivation, deep cognitive engagement, course grade, and conceptual change. Motivational variables were linked to cognitive engagement, course grade, and conceptual change. Finally, students’ course grade was linked to their conceptual change. Need for cognition, boredom, and anxiety played no role in the model. An alternative, revised model was presented excluding these three variables. Theoretical and practical implications of the study are discussed.

Keywords

Conceptual change Structural equation modeling Emotions Motivation Engagement 

References

  1. Abuhamdeh, S., & Csikszentmihalyi, M. (2009). Intrinsic and extrinsic motivational orientations in the competitive context: An examination of person–situation interactions. Journal of Personality, 77, 1615–1635.CrossRefGoogle Scholar
  2. Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84–94.CrossRefGoogle Scholar
  3. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.CrossRefGoogle Scholar
  4. Broughton, S. H., Sinatra, G. M., & Nussbaum, E. M. (2013). Pluto has been a planet my whole life! Emotions, attitudes, and conceptual change in elementary students’ learning about Pluto’s reclassification. Research in Science Education, 43(2), 529–550.CrossRefGoogle Scholar
  5. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.Google Scholar
  6. Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116–131.CrossRefGoogle Scholar
  7. Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, B. W. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in the need for cognition. Psychological Bulletin, 119, 197–253.CrossRefGoogle Scholar
  8. Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of “need for cognition”. Journal of Personality Assessment, 48, 306–307.CrossRefGoogle Scholar
  9. Chi, M. T. H., & Roscoe, R. D. (2002). The processes and challenges of conceptual change. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 3–27). Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
  10. Christensen, W. M., Meltzer, D. E., & Ogilvie, C. A. (2009). Student ideas regarding entropy and the second law of thermodynamics in an introductory physics course. American Journal of Physics, 77, 907–917.CrossRefGoogle Scholar
  11. Cordova, J. R., Sinatra, G. M., Jones, S. H., Taasoobshirazi, G., & Lombardi, D. (2014). Confidence in prior knowledge, self-efficacy, interest and prior knowledge: Influences on conceptual change. Contemporary Educational Psychology, 39(2), 164–174.CrossRefGoogle Scholar
  12. Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self- determination perspective. Educational Psychologist, 26(3 & 4), 325–346.CrossRefGoogle Scholar
  13. Dole, J. A., & Sinatra, G. M. (1998). Reconceptualizing change in the cognitive construction of knowledge. Educational Psychologist, 33, 109–128.CrossRefGoogle Scholar
  14. Duncan, T., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40, 117–128.CrossRefGoogle Scholar
  15. Elliot, A. J., & Moller, A. (2003). Performance-approach goals: Good or bad forms of regulation? International Journal of Educational Research, 39, 339–356.CrossRefGoogle Scholar
  16. Elliot, A. J., & Murayama, K. (2008). On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100, 613–628.CrossRefGoogle Scholar
  17. 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(1), 59–109.CrossRefGoogle Scholar
  18. Frenzel, A. C., Pekrun, R., & Goetz, T. (2007). Girls and mathematics-a “hopeless” issue? A control-value approach to gender differences in emotions towards mathematics. European Journal of Psychology of Education, 22, 497–514.CrossRefGoogle Scholar
  19. Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2007). Nonscience majors learning science: A theoretical model of motivation. Journal of Research in Science Teaching, 44, 1088–1107.CrossRefGoogle Scholar
  20. Gobert, J. D., Baker, R. S., & Wixon, M. B. (2015). Operationalizing and detecting disengagement within online science microworlds. Educational Psychologist, 50(1), 43–57.CrossRefGoogle Scholar
  21. Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 1–17.CrossRefGoogle Scholar
  22. Greene, B. A., Dillon, C., & Crynes, B. (2003). Distributing learning in introductory chemical engineering: University students’ learning, motivation, and attitudes using a CD-ROM. Journal of Educational Computing Research, 29, 189–207.CrossRefGoogle Scholar
  23. Greene, B. A., Miller, R. B., Crowson, H. M., Duke, B. L., & Akey, K. L. (2004). Predicting high school students’ cognitive engagement and achievement: Contributions of classroom perceptions and motivation. Contemporary Educational Psychology, 29, 462–482.CrossRefGoogle Scholar
  24. Gregoire, M. (2003). Is it a challenge or a threat? A dual-process model of teachers’ cognition and appraisal processes during conceptual change. Educational psychology review, 15(2), 147–179.CrossRefGoogle Scholar
  25. Halawah, I. (2006). The effect of motivation, family environment, and student characteristics on academic achievement. Journal of Instructional Psychology, 33, 91–99.Google Scholar
  26. Hannula, M. S. (2006). Motivation in mathematics: Goals reflected in emotions. Educational Studies in Mathematics, 63, 165–178.CrossRefGoogle Scholar
  27. Heddy, B. C., & Sinatra, G. M. (2013). Transforming misconceptions: Using transformative experience to promote positive affect and conceptual change in students learning about biological evolution. Science Education, 97(5), 723–744.CrossRefGoogle Scholar
  28. Hestenes, D., Wells, M., & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30, 141–158.CrossRefGoogle Scholar
  29. Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural equation modeling (pp. 158–176). Thousand Oaks, CA: Sage.Google Scholar
  30. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional fit criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  31. Husman, J., & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34, 113–125.CrossRefGoogle Scholar
  32. Johnson, M. L., & Sinatra, G. M. (2013). Use of task-value instructional inductions for facilitating engagement and conceptual change. Contemporary Educational Psychology, 38(1), 51–63.CrossRefGoogle Scholar
  33. Kaschalk, R. (2002). Physics—why bother?… that’s why! Contextual Teaching Exchange, 1, 1–8.Google Scholar
  34. Keith, T. Z. (1993). Causal influences on school learning. In H. J. Walberg (Ed.), Analytic methods for educational productivity (pp. 21–47). Greenwich, CT: JAI Press.Google Scholar
  35. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford Press.Google Scholar
  36. Knight, R. D. (2002). Five easy lessons: Strategies for successful physics teaching. San Francisco: Addison Wesley.Google Scholar
  37. Liem, A., Lau, S., & Nie, Y. (2008). The role of self-efficacy, task value, and achievement goals in predicting learning strategies, task disengagement, peer relationship, and achievement outcome. Contemporary Educational Psychology, 33, 486–512.CrossRefGoogle Scholar
  38. Linnenbrink, E. A., & Pintrich, P. R. (2002). Motivation as an enabler for academic success. The School Psychology Review, 31, 313–327.Google Scholar
  39. Linnenbrink, E. A., & Pintrich, P. R. (2004). Role of affect in cognitive processing in academic contexts. In D. Y. Dai (Ed.), Motivation, emotion, and cognition: integrative perspectives on intellectual development and functioning (pp. 57–87). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  40. Lombardi, D., & Sinatra, G. M. (2013). Emotions when teaching about human-induced climate change. International Journal of Science Education, 35, 167–191.CrossRefGoogle Scholar
  41. Lombardi, D., Sinatra, G. M., & Nussbaum, E. M. (2013). Plausibility reappraisals and shifts in middle school students’ climate change conceptions. Learning and Instruction, 27, 50–62.CrossRefGoogle Scholar
  42. Mason, L., Boldrin, A., & Vanzetta, A. (2006, August). Epistemological beliefs and achievement goals in conceptual change learning. Paper presented at EARLI conference, StockholmGoogle Scholar
  43. Mazur, E. (1997). Peer instruction. Upper Saddle River, NJ: Addison-Wesley.Google Scholar
  44. Meece, J. L., Blumenfeld, P. C., & Hoyle, R. H. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80(4), 514.CrossRefGoogle Scholar
  45. Miller, B. W. (2015). Using reading times and eye-movements to measure cognitive engagement. Educational Psychologist, 50(1), 31–42.CrossRefGoogle Scholar
  46. Neumann, K., & Welzel, M. (2007). A new labwork course for physics students: Devices, methods, and research projects. European Journal of Physics, 28, 61–69.CrossRefGoogle Scholar
  47. Nussbaum, E. M., & Sinatra, G. M. (2003). Argument and conceptual engagement. Contemporary Educational Psychology, 28(3), 384–395.CrossRefGoogle Scholar
  48. Pajares, F., & Miller, M. D. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of Educational Psychology, 86, 193–203.CrossRefGoogle Scholar
  49. Pedhazur, E. J. (1997). Multiple regression in behavioral research (3rd ed.). Orlando, FL: Harcourt Brace College Publishers.Google Scholar
  50. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.CrossRefGoogle Scholar
  51. Pekrun, R., Elliot, A. J., & Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101(1), 115.CrossRefGoogle Scholar
  52. Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: Exploring control-value antecedents and performance outcomes of neglected emotions. Journal of Educational Psychology, 102, 531–549.CrossRefGoogle Scholar
  53. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48.CrossRefGoogle Scholar
  54. Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37, 91–105.CrossRefGoogle Scholar
  55. Pekrun, R., & Linnenbrink-Garcia, L. (2012). Academic emotions and student engagement. In S. L. Christensen, A. L. Reschley, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). New York: Springer.CrossRefGoogle Scholar
  56. Pekrun, R., & Stephens, E. J. (2009). Goals, emotions, and emotion regulation: Perspectives of the control-value theory. Human Development, 52, 357–365.CrossRefGoogle Scholar
  57. Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544–555.CrossRefGoogle Scholar
  58. Pintrich, P. R., Conley, A. M., & Kempler, T. M. (2003). Current issues in achievement goal theory and research. International Journal of Educational Research, 39, 319–337.CrossRefGoogle Scholar
  59. Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: the role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63, 167–199.CrossRefGoogle Scholar
  60. Pintrich, P. R., & Schunk, D. H. (2002). Motivation in Education: Theory, research, and applications. Upper Saddle River, NJ: Merrill Prentice Hall.Google Scholar
  61. Rayner, A. (2005). Reflections on context based science teaching: A case study of physics students for physiotherapy. Poster presented at the annual UniServe Science Blended Learning Symposium Proceedings, SydneyGoogle Scholar
  62. Reiner, M., Slotta, J. D., Chi, M. T. H., & Resnick, L. B. (2000). Naive physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction, 18, 1–35.CrossRefGoogle Scholar
  63. Ryu, S., & Lombardi, D. (2015). Coding classroom interactions for collective and individual engagement. Educational Psychologist, 50(1), 70–83.CrossRefGoogle Scholar
  64. Savinainen, A., & Scott, P. (2002). The Force Concept Inventory: a tool for monitoring student learning. Physics Education, 37, 45–52.CrossRefGoogle Scholar
  65. Savinainen, A., Scott, P., & Viiri, J. (2005). Using a bridging representation and social interactions to foster conceptual change: Designing and evaluating an instructional sequence for Newton’s third law. Science Education, 89, 175–195.CrossRefGoogle Scholar
  66. Senko, C., Hulleman, C. S., & Harackiewicz, J. M. (2011). Achievement Goal Theory at the Crossroads: Old Controversies, Current Challenges, and New Directions. Educational Psychologist, 46, 26–47.CrossRefGoogle Scholar
  67. Sinatra, G. M., Broughton, S. H., & Lombardi, D. (2014). Emotions in science education. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 415–436). New York: Taylor & Francis.Google Scholar
  68. Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50(1), 1–13.CrossRefGoogle Scholar
  69. Sinatra, G. M., & Pintrich, P. R. (2003). Intentional conceptual change. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  70. Slotta, J. D., & Chi, M. T. H. (2006). Helping students understand challenging topics in science through ontology training. Cognition and Instruction, 24, 261–289.CrossRefGoogle Scholar
  71. Taasoobshirazi, G., & Sinatra, G. M. (2011). A structural equation model of conceptual change in physics. Journal of Research in Science Teaching, 48, 901–918.CrossRefGoogle Scholar
  72. Tippett, C. D. (2010). Refutation text in science education: A review of two decades of research. International Journal of Science and Mathematics Education, 8(6), 951–970.CrossRefGoogle Scholar
  73. Van Yperen, N. W., Elliot, A. J., & Anseel, F. (2009). The influence of mastery-avoidance goals on performance improvement. European Journal of Social Psychology, 39, 932–943.CrossRefGoogle Scholar
  74. Vosniadou, S. (2007). The cognitive-situative divide and the problem of conceptual change. Educational Psychologist, 42, 55–66.CrossRefGoogle Scholar
  75. Walker, C. O., Greene, B. A., & Mansell, R. A. (2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learning and Individual Differences, 16(1), 1–12.CrossRefGoogle Scholar
  76. Wigfield, A., Eccles, J. S., Schiefele, U., & Roeser, R. (2008). Development of achievement motivation. In W. Damon & R. M. Lerner (Eds.), Child and adolescent development: An advanced course (pp. 406–434). New York: Wiley.Google Scholar
  77. Wolters, C., & Pintrich, P. (2001). Contextual differences in student motivation and self- regulated learning in mathematics, English, and social studies classrooms. In H. Hartman (Ed.), Metacognition in learning and instruction (pp. 103–124). Boston, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
  78. Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Gita Taasoobshirazi
    • 1
  • Benjamin Heddy
    • 2
  • MarLynn Bailey
    • 3
  • John Farley
    • 4
  1. 1.Quantitative and Mixed Methods Research MethodologiesUniversity of CincinnatiCincinnatiUSA
  2. 2.Instructional Psychology and TechnologyUniversity of OklahomaNormanUSA
  3. 3.Secondary & Middle Grades EducationKennesaw State UniversityKennesawUSA
  4. 4.Department of Physics and AstronomyUniversity of Nevada, Las VegasLas VegasUSA

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