Predictors of students’ intrinsic motivation during practical work in physics

References

• Abrahams, I. (2007). An unrealistic image of science. School Science Review, 88(324), 119–122.
   Google Scholar
• Abrahams, I. (2009). Does practical work really motivate? A study of the affective value of practical work in secondary school science. International Journal of Science Education, 31(17), 2335–2353. https://doi.org/10.1080/09500690802342836
   Web of Science ®Google Scholar
• Arthur, C., Shepherd, L., & Sumo, M. (2006). The role of students’ diligence in predicting academic performance. Research in the Schools, 13(2), 72–80.
   Google Scholar
• Baker, J. P., & Goodboy, A. K. (2019). The choice is yours: The effects of autonomy-supportive instruction on students’ learning and communication. Communication Education, 68(1), 80–102. https://doi.org/10.1080/03634523.2018.1536793
   Web of Science ®Google Scholar
• Banchi, H., & Bell, R. (2008). The many levels of inquiry. Science and Children, 46(2), 26–29.
   Google Scholar
• Bennett, J. (2003). Teaching and learning science: A guide to recent research and its applications. Continuum.
   Google Scholar
• Bentler, P. M. (2005). Eqs 6 structural equations program manual. Multivariate Software, Encino.
   Google Scholar
• Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model Fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005
   Web of Science ®Google Scholar
• Byrne, B. M. (2010). Multivariate applications series. Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge/Taylor & Francis Group.
   Google Scholar
• Cain, M. K., Zhang, Z., & Yuan, K.-H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1
   PubMed Web of Science ®Google Scholar
• Center of Self-Determination Theory. (n.d.). Intrinsic motivation inventory (IMI). Retrieved January 17, 2022, from, https://selfdeterminationtheory.org/intrinsic-motivation-inventory/
   Google Scholar
• Cerini, B., Murray, I., & Reiss, M. (2003). Student review of the science curriculum: Major findings. (tech. Rep.). planet science, Institute of Education, Science Museum, University of London. https://doi.org/10.13140/RG.2.2.18429.31206
   Google Scholar
• Chen, F. F. (2007). Sensitivity of goodness of Fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834
   Web of Science ®Google Scholar
• Curran, P., West, S., & Finch, J. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29. https://doi.org/10.1037/1082-989X.1.1.16
   Web of Science ®Google Scholar
• Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer.
   Google Scholar
• Department of Physics Education. (2020). Research on intrinsic motivation in experimental activities. https://www.mff.cuni.cz/en/kdf/research-and-cooperation/research-on-intrinsic-motivation-in-experimental-activities
   Google Scholar
• Department of Physics Education. (n.d.). Interactive physics laboratory. Retrieved October 10, 2022, from https://www.mff.cuni.cz/en/kdf/events-for-schools/interactive-physics-laboratory
   Google Scholar
• Erickson, M., Marks, D., & Karcher, E. (2020). Characterizing student engagement with hands-on, problem-based, and lecture activities in an introductory college course. Teaching & Learning Inquiry, 8(1), 138–153. https://doi.org/10.20343/teachlearninqu.8.1.10
   Web of Science ®Google Scholar
• Farland-Smith, D., Finson, K. D., & Arquette, C. M. (2017). How picture books on the national science teacher's association recommend list portray scientists. School Science and Mathematics, 117(6), 250–258. https://doi.org/10.1111/ssm.12231
   Google Scholar
• Gao, S., Mokhtarian, P., & Johnston, R. (2008). Nonnormality of data in structural equation models. Transportation Research Record: Journal of the Transportation Research Board, 2082(1), 116–124. https://doi.org/10.3141/2082-14
   Web of Science ®Google Scholar
• Gardner, P., & Gauld, C. (1990). Labwork and students’ attitudes. In E. Hegarty-Hazel (Ed.), The student laboratory and the science curriculum (pp. 132–156). Routledge.
   Google Scholar
• González, MÁ, & González, MÁ. (2016). Physics in your pocket: Doing experiments and learning With your smartphone. In L.-J. Thoms, & R. Girwidz (Eds.), Selected papers from the 20th international conference on multimedia in physics teaching and learning (pp. 179–185). European Physical Society.
   Google Scholar
• González, MÁ, González, MÁ, Martín, M. E., Llamas, C., Martínez, Ó, Vegas, J., Herguedas, M., & Hernández, C. (2015). Teaching and learning physics with smartphones. Journal of Cases on Information Technology, 17(1), 31–50. https://doi.org/10.4018/JCIT.2015010103
   Google Scholar
• Gustafsson, P. (2005). Gender inclusive physics education—a distance case. European Journal of Physics, 26(5), 843–849. https://doi.org/10.1088/0143-0807/26/5/017
   Web of Science ®Google Scholar
• Harlen, W. (1999). Purposes and procedures for assessing science process skills. Assessment in Education: Principles, Policy & Practice, 6(1), 129–144. https://doi.org/10.1080/09695949993044
   Google Scholar
• Hidi, S., & Harackiewicz, J. M. (2000). Motivating the academically unmotivated: A critical issue for the 21st century. Review of Educational Research, 70(2), 151–179. https://doi.org/10.3102/00346543070002151
   Web of Science ®Google Scholar
• Hochberg, K., Kuhn, J., & Müller, A. (2018). Using smartphones as experimental tools—effects on interest, curiosity, and learning in physics education. Journal of Science Education and Technology, 27(5), 385–403. https://doi.org/10.1007/s10956-018-9731-7
   Web of Science ®Google Scholar
• Hodson, D. (1990). A critical look at practical work in school science. School Science Review, 71(256), 33–40.
   Google Scholar
• Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model Fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
   Google Scholar
• Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
   Web of Science ®Google Scholar
• Inzlicht, M., Shenhav, A., & Olivola, C. Y. (2018). The effort paradox: Effort is both costly and valued. Trends in Cognitive Sciences, 22(4), 337–349. https://doi.org/10.1016/j.tics.2018.01.007
   PubMed Web of Science ®Google Scholar
• Itzek-Greulich, H., & Vollmer, C. (2017). Emotional and motivational outcomes of lab work in the secondary intermediate track: The contribution of a science center outreach lab. Journal of Research in Science Teaching, 54(1), 3–28. https://doi.org/10.1002/tea.21334
   Web of Science ®Google Scholar
• Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409–426. https://doi.org/10.1007/BF02291366
   Web of Science ®Google Scholar
• Kácovský, P., & Snětinová, M. (2021). Physics demonstrations: Who Are the students appreciating them? International Journal of Science Education, 43(4), 529–551. https://doi.org/10.1080/09500693.2020.1871526
   Web of Science ®Google Scholar
• Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
   Google Scholar
• Kokott, K., Lengersdorf, D., & Schlüter, K. (2018). Gender construction in experiment-based biology lessons. Education Sciences, 8(3), 115. https://doi.org/10.3390/educsci8030115
   Web of Science ®Google Scholar
• Milfont, T., & Fischer, R. (2010). Testing measurement invariance across groups: Applications in cross-cultural research. International Journal of Psychological Research, 3(1), 111–130. https://doi.org/10.21500/20112084.857
   Google Scholar
• Millar, R. (2010). Practical work. In J. Osborne, & J. Dillon (Eds.), Good practice in science teaching: What research has to say (2nd ed, pp. 108–134). Open University Press.
   Google Scholar
• Mindrilla, D. (2010). Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: A comparison of estimation bias with ordinal and multivariate Non-normal data. International Journal for Digital Society, 1(1), 60–66. https://doi.org/10.20533/ijds.2040.2570.2010.0010
   Google Scholar
• Monteiro, V., Mata, L., & Peixoto, F. (2015). Intrinsic motivation inventory: Psychometric properties in the context of first language and mathematics learning. Psicologia: Reflexão E Crítica, 28(3), 434–443. https://doi.org/10.1590/1678-7153.201528302
   Web of Science ®Google Scholar
• Mueller, R., & Hancock, G. (2008). Best practices in structural equation modeling. In J. Osborne (Ed.), Best practices in quantitative methods (pp. 488–508). SAGE Publications, Inc.
   Google Scholar
• Murphy, P. (1993). Gender differences in pupils’ reactions to practical work. In R. Levinson (Ed.), Teaching science (pp. 138–150). Routledge.
   Google Scholar
• Niemand, T., & Mai, R. (2018). Flexible cutoff values for fit indices in the evaluation of structural equation models. Journal of the Academy of Marketing Science, 46(6), 1148–1172. https://doi.org/10.1007/s11747-018-0602-9
   Web of Science ®Google Scholar
• Owen, S., Dickson, D., Stanisstreet, M., & Boyes, E. (2008). Teaching physics: Students’ attitudes towards different learning activities. Research in Science & Technological Education, 26(2), 113–128. https://doi.org/10.1080/02635140802036734
   Google Scholar
• Pavelková, I., Škaloudová, A., & Hrabal, V. (2010). Analýza vyučovacích předmětů na základě výpovědí žáků. [Analysis of school subjects on the basis of the testimony of pupils]. Pedagogika, 60(1), 38–61.
   Google Scholar
• Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for social sciences (6th ed.). Routledge.
   Google Scholar
• Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the Art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004
   PubMed Web of Science ®Google Scholar
• Raudenská, P. (2020). The cross-country and cross-time measurement invariance of positive and negative affect scales: Evidence from European social survey. Social Science Research, 86, 102369–12. https://doi.org/10.1016/j.ssresearch.2019.102369
   PubMed Web of Science ®Google Scholar
• Renninger, K. A., & Hidi, S. E. (2016). The power of interest for motivation and engagement (1st ed.). Routledge.
   Google Scholar
• Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43(3), 450–461. https://doi.org/10.1037/0022-3514.43.3.450
   Web of Science ®Google Scholar
• Ryan, R. M., & Deci, E. L. (2000). Self-Determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
   PubMed Web of Science ®Google Scholar
• Ryan, R. M., & Deci, E. L. (2017). Self-Determination theory: Basic psychological needs in motivation, development, and wellness. The Guilford Press.
   Google Scholar
• Ryan, R. M., & Deci, E. L. (2019). Advances in motivation science. Advances in Motivation Science, 6, 111–156. https://doi.org/10.1016/bs.adms.2019.01.001
   Google Scholar
• Schiefele, U. (2009). Situational and individual interest. In K. R. Wentzel, & A. Wigfield (Eds.), Handbook of motivation at school (pp. 197–222). Routledge.
   Google Scholar
• Shin, M., & Johnson, Z. D. (2021). From student-to-student confirmation to students’ self-determination: An integrated peer-centered model of self-determination theory in the classroom. Communication Education, 70(4), 365–383. https://doi.org/10.1080/03634523.2021.1912372
   Web of Science ®Google Scholar
• Snětinová, M., & Kácovský, P. (2019). Interactive physics laboratory: A place for hands-on experimenting. AIP Conference Proceedings 2152 (Proceedings of the 21st DIDFYZ Conference 2019), 030031.
   Google Scholar
• Walkerdine, V. (1998). Counting girls Out (1st ed.). Routledge.
   Google Scholar
• West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with Non normal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56–75). Sage.
   Google Scholar
• Wong, B., DeWitt, J., & Chiu, Y.-L. T. (2021). Mapping the eight dimensions of the ideal student in higher education. Educational Review, 1–19. https://doi.org/10.1080/00131911.2021.1909538
   Google Scholar