Research Article

Measuring Science Teachers' Emotional Experiences with Evolution using Real World Scenarios

William Romine 1 * , Rutuja Mahajan 1 , Amber Todd 1
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1 Wright State University, USA* Corresponding Author
Eurasian Journal of Science and Environmental Education, 1(1), December 2021, 1-26, https://doi.org/10.30935/ejsee/11868
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ABSTRACT

Low acceptance of evolution remains an obstacle to quality biology instruction. We develop and utilize a novel assessment which measures emotional experience in light of real-world evolution education scenarios. We presented 296 science teachers 4 pro-evolution and 8 anti-evolution scenarios and asked them to rate their levels of joy, anger, sadness, fear, disgust, shame, and guilt elicited by that scenario on an ordinal 5-point scale. We used exploratory factor analysis to extract the most important dimensions in the teachers’ responses, Rasch analysis to explore the validity of the extracted subscales, and stepwise regression to find the most important factors driving emotional dispositions. We extracted 3 factors: (1) pro-evolution experience (positive emotions on pro-evolution and negative emotions on anti-evolution scenarios), (2) anti-evolution experience (negative emotions on pro-evolution and positive emotions on anti-evolution scenarios), and (2) feelings of regret over anti-evolution scenarios (shame and guilt on anti-evolution scenarios). Acceptance of evolution facts and a non-theistic religious orientation were positively related to pro-evolution experience. Anti-evolution experience was predicted by lack of microevolution acceptance and lack of teacher preparation. Feelings of regret around anti-evolution scenarios were driven by acceptance of evolution facts and lower levels of teacher preparation. This work advances our understanding of how teachers relate affectively to the theory of evolution and offers empirical insight into ways to improve dispositions about evolution.
Keywords: emotion, evolution acceptance, evolution education, measurement, psychometric

CITATION (APA)

Romine, W., Mahajan, R., & Todd, A. (2021). Measuring Science Teachers' Emotional Experiences with Evolution using Real World Scenarios. Eurasian Journal of Science and Environmental Education, 1(1), 1-26. https://doi.org/10.30935/ejsee/11868

REFERENCES

  1. Arribas-Ayllon, M., & Walkerdine, V. (2017). Foucauldian discourse analysis. In C. Willig & W. Rogers. The Sage handbook of qualitative research in psychology (2nd ed., pp. 91-108). Sage. https://doi.org/10.4135/9781526405555.n7
  2. Barnes, M. E., Roberts, J. A., Maas, S. A., & Brownell, S. E. (2021). Muslim undergraduate biology students’ evolution acceptance in the United States. Plos One, 16(8), 1-20. https://doi.org/10.1371/journal.pone.0255588
  3. Barnes, M. E., Supriya, K., Zheng, Y., Roberts, J. A., & Brownell, S. E. (2021). A new measure of students’ perceived conflict between evolution and religion (PCoRE) is a stronger predictor of evolution acceptance than understanding or religiosity. CBE—Life Sciences Education, 20(3), 1-16. https://doi.org/10.1187/cbe.21-02-0024
  4. Bartlett, M. S. (1950). Tests of significance in factor analysis. British Journal of Psychology, 3, 77-85. https://doi.org/10.1111/j.2044-8317.1950.tb00285.x
  5. Beard, J., Nelson, C., & Nickels, M. (2014). Evolution and the nature of science institutes (ENSI). Indiana University. https://ensiweb.bio.indiana.edu/index.html
  6. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238. https://doi.org/10.1037/0033-2909.107.2.238
  7. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588. https://doi.org/10.1037/0033-2909.88.3.588
  8. Berkman, M. B., Pacheco, J. S., & Plutzer, E. (2008). Evolution and creationism in America's classrooms: a national portrait. PLOS Biology, 6(5), 920-924. https://doi.org/10.1371/journal.pbio.0060124
  9. Bond, T., & Fox, C. M. (2013). Applying the Rasch model: fundamental measurement in the human sciences (2nd ed). Routledge. https://doi.org/10.4324/9781410614575
  10. Boone, W. J., & Scantlebury, K. (2006). The role of Rasch analysis when conducting science education research utilizing multiple‐choice tests. Science Education, 90(2), 253-269. https://doi.org/10.1002/sce.20106
  11. Bowman, K. L. (2008). The evolution battles in high‐school science classes: Who is teaching what?. Frontiers in Ecology and the Environment, 6(2), 69-74. https://doi.org/10.1890/070013
  12. Bradley, M. M., & Lang, P. J. (2000). Measuring emotion: Behavior, feeling, and physiology. In R. Lane & L. Nadel (Eds.) Cognitive neuroscience of emotion (pp. 242-276). Oxford.
  13. Brewer, C. A., & Smith, D. (2011). Vision and change in undergraduate biology education: A call to action. American Association for the Advancement of Science. https://visionandchange.org/finalreport/
  14. Buja, A., & Eyuboglu, N. (1992). Remarks on parallel analysis. Multivariate behavioral research, 27(4), 509-540. https://doi.org/10.1207/s15327906mbr2704_2
  15. Cleaves, A., & Toplis, R. (2007). In the shadow of Intelligent Design: the teaching of evolution. Journal of Biological Education, 42(1), 30-35. https://doi.org/10.1080/00219266.2007.9656104
  16. Cobern, W. W. (1994). Comments and criticism. Point: Belief, understanding, and the teaching of evolution. Journal of Research in Science Teaching, 31(5), 583-590. https://doi.org/10.1002/tea.3660310511
  17. Collins, L. M., & Lanza S. T. (2010) Latent class and latent transition analysis: with applications in the social, behavioral, and health sciences (vol. 718). Wiley.
  18. Corwin, L. A., Runyon, C., Robinson, A., & Dolan, E. L. (2015). The laboratory course assessment survey: a tool to measure three dimensions of research-course design. CBE—Life Sciences Education, 14(4), 1-11. https://doi.org/10.1187/cbe.15-03-0073
  19. Czerniak, C. M., & Schriver, M. L. (1994). An examination of preservice science teachers’ beliefs and behaviors as related to self-efficacy. Journal of Science Teacher Education, 5(3), 77-86. https://doi.org/10.1007/BF02614577
  20. Darwin, C. (1872). The expression of the emotions in man and animals. John Murray. https://doi.org/10.7208/9780226220802
  21. De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford Publications.
  22. Deniz, H., Donnelly, L. A., & Yilmaz, I. (2008). Exploring the factors related to acceptance of evolutionary theory among Turkish preservice biology teachers: Toward a more informative conceptual ecology for biological evolution. Journal of Research in Science Teaching, 45(4), 420-443. https://doi.org/10.1002/tea.20223
  23. Donnelly, L. A., Kazempour, M., & Amirshokoohi, A. (2009). High school students’ perceptions of evolution instruction: acceptance and evolution learning experiences. Research in Science Education, 39(5), 643-660. https://doi.org/10.1007/s11165-008-9097-6
  24. Eastwell, P., & MacKenzie, A. H. (2009). Inquiry learning: Elements of confusion and frustration. The American Biology Teacher, 71(5), 263-266. https://doi.org/10.2307/27669426
  25. Ekman, P. E., & Davidson, R. J. (1994). The nature of emotion: Fundamental questions. Oxford University Press.
  26. Fouad, K. E. (2018). Pedagogical implications of American Muslims’ Views on Evolution. In H. Deniz & L. Borgerding (Eds). Evolution education around the globe (pp. 15-39). Springer. https://doi.org/10.1007/978-3-319-90939-4_2
  27. Fowler, S. R., Zeidler, D. L., & Sadler, T. D. (2009). Moral sensitivity in the context of socioscientific issues in high school science students. International Journal of Science Education, 31(2), 279-296. https://doi.org/10.1080/09500690701787909
  28. Frijda, N. H. (1986). The Emotions. Cambridge University Press.
  29. Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2007). Nonscience majors learning science: A theoretical model of motivation. Journal of Research in Science Teaching, 44(8), 1088-1107. https://doi.org/10.1002/tea.20181
  30. Gottman, J. M., Murray, J. D., Swanson, C. C., Tyson, R. & Swanson, K. R. (2005). The mathematics of marriage: Dynamic nonlinear models. MIT Press.
  31. Gough, N. (2021). The Christian right’s war on reality: Where do/should American science teachers stand? Cultural Studies of Science Education, 16, 1-10. https://doi.org/10.1007/s11422-021-10058-4
  32. Griffith, J. A., & Brem, S. K. (2004). Teaching evolutionary biology: Pressures, stress, and coping. Journal of Research in Science Teaching, 41(8), 791-809. https://doi.org/10.1002/tea.20027
  33. Gruber, J., Oveis, C., Keltner, D., & Johnson, S. L. (2011). A discrete emotions approach to positive emotion disturbance in depression. Cognition and Emotion, 25(1), 40-52. https://doi.org/10.1080/02699931003615984
  34. Ha, M., Haury, D. L., & Nehm, R. H. (2012). Feeling of certainty: Uncovering a missing link between knowledge and acceptance of evolution. Journal of Research in Science Teaching, 49(1), 95-121. https://doi.org/10.1002/tea.20449
  35. Hair, J., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis (4th ed.). Prentice-Hall Inc.
  36. Hargreaves, A. (1998). The emotional politics of teaching and teacher development: With implications for educational leadership. International Journal of Leadership in Education, 1(4), 315-336. https://doi.org/10.1080/1360312980010401
  37. Hawley, P. H., Short, S. D., McCune, L. A., Osman, M. R., & Little, T. D. (2011). What’s the matter with Kansas?: The development and confirmation of the Evolutionary Attitudes and Literacy Survey (EALS). Evolution: Education and Outreach, 4(1), 117-132. https://doi.org/10.1007/s12052-010-0294-1
  38. Hawley, P. H., & Sinatra, G. M. (2019). Declawing the dinosaurs in the science classroom: Reducing Christian teachers’ anxiety and increasing their efficacy for teaching evolution. Journal of Research in Science Teaching, 56(4), 375-401. https://doi.org/10.1002/tea.21479
  39. 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. https://doi.org/10.1002/sce.21072
  40. Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure. British Journal of Statistical Psychology, 17(1), 65-70. https://doi.org/10.1111/j.2044-8317.1964.tb00244.x
  41. Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393-416. https://doi.org/10.1177/0013164405282485
  42. Hill, J. P. (2014). Rejecting evolution: The role of religion, education, and social networks. Journal for the Scientific Study of Religion, 53(3), 575-594. https://doi.org/10.1111/jssr.12127
  43. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185. https://doi.org/10.1007/BF02289447
  44. Hu, L. T., & 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
  45. Izard, C. E. (1991). The psychology of emotions. Plenum Press. https://psycnet.apa.org/doi/10.1007/978-1-4899-0615-1
  46. Johnson, R. L., & Peeples, E. E. (1987). The role of scientific understanding in college: student acceptance of evolution. The American Biology Teacher, 49(2), 93-98. https://doi.org/10.2307/4448445
  47. Jolliffe, I. (2011). Principal component analysis. Springer.
  48. Kaiser, H. F. (1970). A Second-Generation Little Jiffy. Psychometrika, 35(4), 401-15. https://doi.org/10.1007/BF02291817
  49. Lakatos, I. (1976). Falsification and the methodology of scientific research programmes: Can theories be refuted? Springer. https://doi.org/10.1007/978-94-010-1863-0_14
  50. Lamb, R. L., Annetta, L., Meldrum, J., & Vallett, D. (2012). Measuring science interest: Rasch validation of the science interest survey. International Journal of Science and Mathematics Education, 10(3), 643-668. https://doi.org/10.1007/s10763-011-9314-z
  51. Lane, R. D., Chua, P. M., & Dolan, R. J. (1999). Common effects of emotional valence, arousal and attention on neural activation during visual processing of pictures. Neuropsychologia, 37(9), 989-997. https://doi.org/10.1016/S0028-3932(99)00017-2
  52. Lang, P. J. (1995). The emotion probe: studies of motivation and attention. American Psychologist, 50(5), 372. https://doi.org/10.1037/0003-066X.50.5.372
  53. Lasky, S. (2000). The cultural and emotional politics of teacher–parent interactions. Teaching and Teacher Education, 16(8), 843-860. https://doi.org/10.1016/S0742-051X(00)00030-5
  54. Linacre J. M. (2006). Winsteps. Mesa Press. https://www.winsteps.com/manuals.htm
  55. Linacre, J. M., & Tennant, A. (2009). More about critical eigenvalue sizes in standardized-residual principal components analysis (PCA). Rasch Measurement Transactions, 23(3), 1228. https://www.rasch.org/rmt/rmt233f.htm
  56. Malone, K. R., & Barabino, G. (2009). Narrations of race in STEM research settings: Identity formation and its discontents. Science Education, 93(3), 485-510. https://doi.org/10.1002/sce.20307
  57. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149-174. https://doi.org/10.1007/BF02296272
  58. Masters, G. N. (1988). Item discrimination: When more is worse. Journal of Educational Measurement, 25(1), 15-29. https://doi.org/10.1111/j.1745-3984.1988.tb00288.x
  59. Muthén B. O., Muthén L. K. (2012). Mplus 7 base program. Muthén & Muthén, Inc. https://bit.ly/3CGgog5
  60. Nadelson, L. S., & Southerland, S. (2012). A more fine-grained measure of students' acceptance of evolution: development of the Inventory of Student Evolution Acceptance—I-SEA. International Journal of Science Education, 34(11), 1637-1666. https://doi.org/10.1080/09500693.2012.702235
  61. National Association of Biology Teachers. (2011). NABT’s statement on teaching evolution. Retrieved February 6, 2019, from https://bit.ly/3Bc6Ckm
  62. Nehm, R. H., & Reilly, L. (2007). Biology majors' knowledge and misconceptions of natural selection. BioScience, 57(3), 263-272. https://doi.org/10.1641/B570311
  63. Nehm, R. H., & Schonfeld, I. S. (2008). Measuring knowledge of natural selection: A comparison of the CINS, an open‐response instrument, and an oral interview. Journal of Research in Science Teaching, 45(10), 1131-1160. https://doi.org/10.1002/tea.20251
  64. NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press. https://doi.org/10.17226/18290
  65. Noland, T. (2021). Teacher guilt: How can it inform instruction in foundational skills in reading (Doctoral dissertation, St. John's University). Sr. John’s Scholar. https://scholar.stjohns.edu/theses_dissertations/248/
  66. O’Connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers, 32(3), 396-402. https://doi.org/10.3758/BF03200807
  67. Osborne, J. W., Costello, A. B., & Kellow, J. T. (2008). Best practices in exploratory factor analysis. In A. B. Costello (Ed.) Best Practices in Quantitative Methods (pp. 6-99). Sage. https://doi.org/10.4135/9781412995627
  68. Palmer, D. H. (2006). Sources of self-efficacy in a science methods course for primary teacher education students. Research in Science Education, 36(4), 337-353. https://doi.org/10.1007/s11165-005-9007-0
  69. Peñaloza, G., El-Hani, C. N., & Mosquera-Suárez, C. J. (2021). Between Scientific Ideas and Christian Religious Beliefs. Science & Education, 30, 931-965. https://doi.org/10.1007/s11191-021-00218-x
  70. Plutzer, E., Branch, G., & Reid, A. (2020). Teaching evolution in US public schools: a continuing challenge. Evolution: Education and Outreach, 13(1), 1-15. https://doi.org/10.1186/s12052-020-00126-8
  71. Popper, K. (1968). Conjectures and Refutations. British Journal for the Philosophy of Science, 19(2),159-168. https://philpapers.org/rec/POPCAR-7
  72. Robbins, J. R., & Roy, P. (2007). The natural selection: identifying & correcting non-science student preconceptions through an inquiry-based, critical approach to evolution. The American Biology Teacher, 69(8), 460-467. https://doi.org/10.2307/4452205
  73. Romine, W. L., Barrow, L. H., & Folk, W. R. (2013). Exploring secondary students' knowledge and misconceptions about influenza: Development, validation, and implementation of a multiple-choice influenza knowledge scale. International Journal of Science Education, 35(11), 1874-1901. https://doi.org/10.1080/09500693.2013.778439
  74. Romine, W. L., Barnett, E., Friedrichsen, P. J., & Sickel, A. J. (2014). Development and evaluation of a model for secondary evolution educators’ professional development needs. Evolution: Education and Outreach, 7(1), 1-10. https://doi.org/10.1186/s12052-014-0027-y
  75. Romine, W. L., & Sadler, T. D. (2016). Measuring changes in interest in science and technology at the college level in response to two instructional interventions. Research in Science Education, 46(3), 309-327. https://link.springer.com/article/10.1007%2Fs11165-014-9452-8
  76. Romine, W. L., Todd, A. N., & Walter, E. M. (2018). A closer look at the items within three measures of evolution acceptance: analysis of the MATE, I-SEA, and GAENE as a single corpus of items. Evolution: Education and Outreach, 11(1), 1-20. https://doi.org/10.1186/s12052-018-0093-7
  77. Romine, W., Schroeder, N., Edwards, A., & Banerjee, T. (2021). Longitudinal classification of mental effort using electrodermal activity, heart rate, and skin temperature data from a wearable sensor. In A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. Farinella, T. Mei, M. Bertini, H. Escalante, & R. Vezzani (Eds.), Pattern Recognition: ICPR International Workshops and Challenges (Part II) (pp. 86–95). Springer.
  78. Romine, W. L., Schroeder, N. L., Graft, J., Yang, F., Sadeghi, R., Zabihimayvan, M., D. Kaderiya, & Banerjee, T. (2020). Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: development of a cognitive load tracker for both personal and classroom use. Sensors, 20(17), 1-14. https://doi.org/10.3390/s20174833
  79. Russell, G. W. (1983). Psychological issues in sports aggression. In J. H. Goldstein (Ed.) Sports violence (pp. 157-181). Springer. https://doi.org/10.1007/978-1-4612-5530-7_10
  80. Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110, 145–172. https://doi.org/10.1037/0033-295X.110.1.145
  81. Rutledge, M. L., & Warden, M. A. (1999). The development and validation of the measure of acceptance of the theory of evolution instrument. School Science and Mathematics, 99(1), 13-18. https://doi.org/10.1111/j.1949-8594.1999.tb17441.x
  82. Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8(1), 3-15. https://doi.org/10.1191/096228099671525676
  83. Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695-729. https://doi.org/10.1177/0539018405058216
  84. Scherer, K. R., & Ceschi, G. (2000). Criteria for emotion recognition from verbal and nonverbal expression: Studying baggage loss in the airport. Personality and Social Psychology Bulletin, 26(3), 327-339. https://doi.org/10.1177/0146167200265006
  85. Scherer, K. R. (2004). Which emotions can be induced by music? What are the underlying mechanisms? And how can we measure them? Journal of New Music Research, 33(3), 239-251. https://doi.org/10.1080/0929821042000317822
  86. Schoon, K. J., & Boone, W. J. (1998). Self‐efficacy and alternative conceptions of science of preservice elementary teachers. Science Education, 82(5), 553-568. https://doi.org/10.1002/(SICI)1098-237X(199809)82:5%3C553::AID-SCE2%3E3.0.CO;2-8
  87. Siani, M., & Yarden, A. (2020). Evolution? I don’t believe in it. Science & Education, 29(2), 411-441. https://doi.org/10.1007/s11191-020-00109-7
  88. Sickel, A. J., & Friedrichsen, P. (2013). Examining the evolution education literature with a focus on teachers: major findings, goals for teacher preparation, and directions for future research. Evolution: Education and Outreach, 6(1), 1-15. https://doi.org/10.1186/1936-6434-6-23
  89. Silva, H. M., Oliveira, A. W., Belloso, G. V., Díaz, M. A., & Carvalho, G. S. (2021). Biology teachers’ conceptions of Humankind Origin across secular and religious countries: an international comparison. Evolution: Education and Outreach, 14(1), 1-12. https://doi.org/10.1186/s12052-020-00141-9
  90. Smith, M. U., Snyder, S. W., & Devereaux, R. S. (2016). The GAENE—generalized acceptance of evolution evaluation: development of a new measure of evolution acceptance. Journal of Research in Science Teaching, 53(9), 1289-1315. https://doi.org/10.1037/t67588-000
  91. Smith, R. M. (1996). Polytomous mean-square fit statistics. Rasch Measurement Transactions, 10(3), 516-517. https://www.rasch.org/rmt/rmt103a.htm
  92. Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42(5), 893-898. https://doi.org/10.1016/j.paid.2006.09.017
  93. Stemmler, G. (2002). Methodological considerations in the psychophysiological study of emotion. In R. J. Davidson, H. H. Goldsmith & K. R. Scherer (Eds.), Handbook of affective science (pp. 225-255). Oxford University Press.
  94. Sterne, J. A., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M. & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. British Medical Journal, 338, 157-160. https://doi.org/10.1136/bmj.b2393
  95. Thompson, J. J., & Windschitl, M. (2005). "Failing girls": Understanding connections among identity negotiation, personal relevance, and engagement in science learning from underachieving girls. Journal of Women and Minorities in Science and Engineering, 11(1), 1-26. https://doi.org/10.1615/JWomenMinorScienEng.v11.i1.10
  96. Trani, R. (2004). I won't teach evolution; it's against my religion. And now for the rest of the story. The American Biology Teacher, 66(6), 419-427. https://doi.org/10.2307/4451708
  97. Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3), 1-13. https://doi.org/10.33151/ajp.8.3.93
  98. Wright, B. D., & Stone, M. H. (1979). Best test design. MESA Press. https://research.acer.edu.au/measurement/1/
  99. Wright, B. D., Linacre, J. M., Gustafson, J. E., & Martin-Loff, P. (1994). Reasonable mean square fit values. Rasch Measurement Transactions, 8(3), 370. https://www.rasch.org/rmt/rmt83b.htm
  100. Wundt, W. (1896). Grundriss der psychologie [Outline of Psychology]. Alfred Kröner Verlag.
  101. Yacoub, S., Simske, S., Lin, X., Burns, J. (2003) Recognition of emotions in interactive voice response systems. In H. Bourland (Ed.), Proceeding of the. 8th European Conference on Speech Communication and Technology (Eurospeech 2003) (pp. 729-732). International Speech Communication Association.
  102. Young, D. J. (1991). Creating a low‐anxiety classroom environment: What does language anxiety research suggest? The Modern Language Journal, 75(4), 426-437. https://doi.org/10.1111/j.1540-4781.1991.tb05378.x
  103. Yuen, C. T., San San, W., Seong, T. C., & Rizon, M. (2009). Classification of human emotions from EEG signals using statistical features and neural network. International Journal of Integrated Engineering, 1(3), 71-79. https://bit.ly/3byxdOm
  104. Zembylas, M. (2002). Constructing genealogies of teachers' emotions in science teaching. Journal of Research in Science Teaching, 39(1), 79-103. https://doi.org/10.1002/tea.10010
  105. Zembylas, M. (2004). Emotional issues in teaching science: A case study of a teacher’s views. Research in Science Education, 34(4), 343-364. https://doi.org/10.1007/s11165-004-0287-6
  106. Zwick, R. W., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432-442. https://doi.org/10.1037/0033-2909.99.3.432