Fundación Universidad de Golfito, Costa Rica
* Corresponding author
Universidad de Costa Rica, Costa Rica
Universidad de Costa Rica, Costa Rica
Universidad de Costa Rica, Costa Rica

Article Main Content

It has been discussed that emotional investment in the educational process generates positive cognitive outcomes. This correlation addresses a core tenet of the three learning domains, Cognitive, Affective, and Psycho-Motor where all three constitute separate areas of one single learning process. And as such, one would expect to be able to measure this correspondence between the three domains. This study attempts to test the hypothesis of a correlation between the cognitive and affective domains in a cohort of in-service science teachers. Specifically, we seek to assess whether the affective posture towards school sciences shows an association with their cognitive competence in biology, chemistry and physics. We used partial least square regression analysis to examine the data, and the results indicate a direct correlation between the affective and cognitive domains. Teachers who scored higher in cognitive tests for the three sciences also had a more positive attitude towards them. These findings provide strong empirical evidence in support of the theoretical principle that the three domains are separate but interconnected components of the educational process.

References

  1. Anderson, L. W. (2005). Objectives, evaluation, and the improvement of education. Studies in Education Evaluation, 31, 102–113.
     Google Scholar
  2. Anderson, L. W., Krathwohl, D. R., Airasian, P., Cruikshank, K., Mayer, R., Pintrich, P., Wittrock, M. (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom’s taxonomy.
     Google Scholar
  3. Artz, A.F., & Armour-Thomas, E. (1992). Development of a cognitive-metacognitive framework for protocol analysis of mathematical problem solving in small groups. Cognition and Instruction (Vol. 9, pp. 137–175). New York: Longman Publishing.
     Google Scholar
  4. Baker, C. (2010). The impact of instructor immediacy and presence for online student affective learning, cognition, and motivation. Journal of Educators Online, 7(1), 1–30. Retrieved from http://eric.ed.gov/?q=EJ904072&id=EJ904072.
     Google Scholar
  5. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ.
     Google Scholar
  6. Bathgate, M., & Schunn, C. D. (2017). Factors that deepen or attenuate decline of science utility value during the middle school years. Contemporary Educational Psychology, 49, 215–225. https://doi.org/10.1016/j.cedpsych.2017.02.005.
     Google Scholar
  7. Bell, D. (2016). The reality of STEM education, design and technology teachers’ perceptions: a phenomenographic study. International Journal of Technology and Design Education, 26, 61–79. https://doi.org/https://doi.org/10.1007/s10798-015-9300-9
     Google Scholar
  8. Betz, N. E., & Hackett, G. (1986). Applications of Self-Efficacy Theory to Understanding Career Choice Behaviour. Journal of Social and Clinical Psychology, 4(3), 279–289. http://doi.org/10.1521/jscp.1986.4.3.279.
     Google Scholar
  9. Bloom, B., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. New York: David McKay Company.
     Google Scholar
  10. Bloom, B. S., Krathwohl, D. R., & Masia, B. B. (1964). Taxonomy of educational objectives: Book 2 affective domain. New York: David McKay&Co Inc.
     Google Scholar
  11. Bucat, R. (2004). Pedagogical Content Knowledge As a Way Forward: Applied Research in Chemistry Education. Chem. Educ. Res. Pract., 5(3), 215–228. http://doi.org/10.1039/B4RP90025A.
     Google Scholar
  12. Chesnut, S. R., & Burley, H. (2015). Self-efficacy as a predictor of commitment to the teaching profession: A meta-analysis. Educational Research Review, 15, 1–16. http://doi.org/10.1016/j.edurev.2015.02.001.
     Google Scholar
  13. Díaz, S., Umaña, R., Quesada, H., Benavides, L., & Sandí, R. (2012). Biología, Programas de Estudio (Vol. 2012). San José: MEP. Retrieved from: http://www.dgb.sep.gob.mx/02-m1/03-iacademica/programasdeestudio.php.
     Google Scholar
  14. Di Rienzo, J. A., Casanoves, F., Balzarini, M. G., Tablada, M., & Robledo, C. W. (2017). InfoStat. Córdoba: InfoStat Group, Universidad Nacional de Córdoba.
     Google Scholar
  15. Dreyfuss, S. E., & Dreyfus, H. L. (1980). A five-stage model of the mental activities involved in directed skill acquisition. Operations Research Center, (February), 1–18. http://doi.org/ADA084551.
     Google Scholar
  16. Grauer, B. (2014). Secondary Science Teachers’ Use of the Affective Domain in Science Education. Kansas State University.
     Google Scholar
  17. Jeong, J. S., & González-Gómez, D. (2021). A STEM Course Analysis During COVID-19: A Comparison Study in Performance and Affective Domain of PSTs Between F2F and F2S Flipped Classroom. Frontiers in Psychology, 12(August), 1–13. https://doi.org/10.3389/fpsyg.2021.669855.
     Google Scholar
  18. Jungert, T., & Rosander, M. (2010). Self-efficacy and strategies to influence the study environment. Teaching in Higher Education, 15(6), 647–659. http://doi.org/10.1080/13562517.2010.522080.
     Google Scholar
  19. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575.
     Google Scholar
  20. Knaus, K., Murphy, K., Blecking, A., & Holme, T. (2011). Assignment of Chemistry Exam Items. Journal of Chemical Education, 554–560.
     Google Scholar
  21. Knuver, A. W. M., & Brandsma, H. P. (1993). Cognitive and Affective Outcomes in School Effectiveness Research. School Effectiveness and School Improvement, 4(3), 189–204. http://doi.org/10.1080/0924345930040302.
     Google Scholar
  22. Krathwohl, D. R., & Anderson, L. W. (2010). Merlin C. Wittrock and the revision of bloom’s taxonomy. Educational Psychologist, 45(1), 64–65. http://doi.org/10.1080/00461520903433562.
     Google Scholar
  23. Kraus, S. J. (1995). Attitudes and the Prediction of Behavior: Meta-Analysis of the empirical literature. Personality and Social Psychology Bulletin, 21(1), 58–75.
     Google Scholar
  24. Levy, D. C. (2015). Costa Rica: Public Continuity, Private Gains. International Higher Education, 43, 22–23.
     Google Scholar
  25. Lin, P. Y., Chai, C. S., & Jong, M. S. Y. (2021). A Study of Disposition, Engagement, Efficacy, and Vitality of Teachers in Designing Science, Technology, Engineering, and Mathematics Education. Frontiers in Psychology, 12(August), 1–12. https://doi.org/10.3389/fpsyg.2021.661631.
     Google Scholar
  26. Martin, B. L., & Briggs, L. J. (1986). The affective and cognitive domains: Integration for instruction and research. Educational Technology.
     Google Scholar
  27. Messick, S. (1987). Validity. ETS Research Report Series (Vol. 1987). Wiley Online Library.
     Google Scholar
  28. P.E.N. (2019). Educación secundaria en Costa Rica. San Jose. Retrieved from: https://www.estadonacion.or.cr/educacion2017/assets/parte-1-capitulo-4.pdf.
     Google Scholar
  29. Pierre, E., & Oughton, J. (2007). The Affective Domain: Undiscovered Country. College Quarterly, 10(4), 1–7. Retrieved from: http://www.eric.ed.gov/ERICWebPortal/detail?accno=EJ813766%5Cnhttp://www.eric.ed.gov/PDFS/EJ813766.pdf.
     Google Scholar
  30. Sanchez, J. M. P. (2019). Indicators of Asian Achievement in Chemistry: Implications to the Philippine Setting. Kimika, 30(1), 18–30. https://doi.org/10.26534/kimika.v30i1.18-30.
     Google Scholar
  31. Sandi-Urena, S. (2018). Phenomenological Approaches to Study Learning in the Tertiary Level Chemistry Laboratory. Química Nova, 41(2), 236–242.
     Google Scholar
  32. Savickiene, I. (2010). Conception of Learning Outcomes in the Bloom’s Taxonomy Affective Domain. Quality of Higher Education, 7, 37–59. http://pgi.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ900258&site=ehost-live&scope=site.
     Google Scholar
  33. Sellin, N. (1995). Partial least squares modeling in research on educational achievement. In W. Bos & R. H. Lehmann (Eds.), Reflections on Educational Achievement (pp. 256–267). Hamburg: Waxmann.
     Google Scholar
  34. Sevilla Solano, C., Calderón Solano, C., Hernández Jiménez, C., & Villalobos Salas, J. (2012). Programas de Estudio CIENCIAS: Tercer Ciclo de Educación Básica. San José: Ministerio de Educación Pública. Spanish.
     Google Scholar
  35. Shoffner, M. (2009). The place of the personal: Exploring the affective domain through reflection in teacher preparation. Teaching and Teacher Education, 25(6), 783–789. http://doi.org/10.1016/j.tate.2008.11.012.
     Google Scholar
  36. Taherdoost, H. (2018). Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research. SSRN Electronic Journal, 5(3), 28–36. https://doi.org/10.2139/ssrn.3205040.
     Google Scholar
  37. Thompson, T. L., & Mintzes, J. J. (2002). Cognitive structure and the affective domain: On knowing and feeling in biology. International Journal of Science Education, 24(6), 645–660. http://doi.org/10.1080/09500690110110115.
     Google Scholar
  38. Valverde, M., Alfaro, G., Navas, M., Castillo, X. M., & Acón, I. (2012). Química, Programa de Estudio. San José: MEP. Spanish.
     Google Scholar
  39. Villalobos, J. M., & Jiménez, R. I. (2012). Física, Programa de Estudios. Programa de Estudios. San José: MEP. Spanish.
     Google Scholar
  40. Walls, R. T., Nardi, A. H., von Minden, A. M., & Hoffman, N. (2002). The Characteristics of Effective and Ineffective Teachers. Teacher Education Quarterly, 29(1), 39–48. Retrieved from http://www.jstor.org/stable/jthought.49.1-2.27.
     Google Scholar
  41. Yada, A., Tolvanen, A., & Savolainen, H. (2017). Teachers’ attitudes and self-efficacy on implementing inclusive education in Japan and Finland: A comparative study using multi-group structural equation modelling. Teaching and Teacher Education, 75, Manuscript submitted for publication. http://doi.org/10.1016/j.tate.2018.07.011.
     Google Scholar
  42. Zimmerman, B. J., & Lebeau, R. B. (2000). A commentary on self-directed learning. In C. E. Evensen, D. H. and Hmelo-Silver (Ed.), Problem-based learning: A research perspective on learning interactions (pp. 299–313). Lawrence Erlbaum Associates Mahwah, NJ.
     Google Scholar