Ecologías de aprendizaje y motivación del profesorado universitario de Ciencias de la Salud

  1. Iris Estévez 1
  2. Alba Souto-Seijo 1
  3. Mercedes González-Sanmamed 1
  4. Antonio Valle 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Journal:
Educación XX1: Revista de la Facultad de Educación

ISSN: 1139-613X,  2174-5374

Year of publication: 2021

Volume: 24

Issue: 2

Pages: 19-42

Type: Article

DOI: 10.5944/EDUCXX1.28660 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Educación XX1: Revista de la Facultad de Educación

Abstract

The present work aims to deepen the study of one of the key components that make up the Personal Dimension of the Learning Ecologies of Health Sciences faculty: teaching motivation. This is a topic scarcely explored in the field of Higher Education, despite the fact that it is an element that strongly influences the quality of the teaching-learning process. Consequently, from a person-centered perspective, the objective of this study is based on the identification of teacher motivational profiles from the combination of two motivational orientations (performance-centered motivation and mastery-oriented motivation). The methodology used was of a quantitative nature, through a survey, and with an exploratory design. The sample is made up of 416 members of the faculty of Health Sciences, belonging to 37 Spanish universities. Using the Latent Class Analysis technique, three motivational teacher profiles were identified: a) Motivated Profile; b) Moderately Motivated Profile; and C) Unmotivated Profile. In the first we find teachers who show high levels of mastery-oriented motivation and moderate levels of performance-oriented motivation. This profile is made up of more than half of the total sample. The second profile is made up of teachers with moderately high motivational levels, although unlike the first, performance-related reasons predominate. Finally, the third group includes teachers who show very low levels in both motivational orientations. These results portray a promising scenario, although potentially perfectible. The implications of this study are aimed at the design and generation of training itineraries better adjusted to the needs and characteristics of the professors to whom they are directed.

Bibliographic References

  • Akaike, H. (1974). A new look at the statistical model identification. En E. Parzen, K. Tanabe & G. Kitagawa (Eds.), Selected Papers of Hirotugu Akaike. Springer Series in Statistics (pp. 716-723). Springer. https:// doi.org/10.1007/978-1-4612-1694-0_16
  • Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development, 49(4), 193–224. https://doi. org/10.1159/000094368
  • Bray, B. C., & Dziak, J. J. (2018). Commentary on latent class, latent profile, and latent transition analysis for characterizing individual differences in learning. Learning and Individual Differences, 66, 105-110. https://doi. org/10.1016/j.lindif.2018.06.001
  • Caballero, K., & Bolívar, A. (2015). El profesorado universitario como docente: hacia una identidad profesional que integre docencia e investigación. REDU. Revista de Docencia Universitaria, 13(1), 57–77. https://bit.ly/318J5m3
  • Castellano-Ramos, C. (2018). Los pensamientos de los profesores universitarios de ciencias de la salud. Concepciones sobre enseñanza y aprendizaje. Palobra, 18, 116-133. https:// bit.ly/3k6jSiL
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2ª Ed.). Erlbaum.
  • Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioural, and health sciences. Wiley. https://doi. org/10.1002/9780470567333
  • Daumiller, M., Stupnisky, R., & Janke, S. (2020). Motivation of higher education faculty: Theoretical approaches, empirical evidence, and future directions. International Journal of Educational Research, 99, 1–6. https:// doi.org/10.1016/j.ijer.2019.101502
  • Esdar, W., Gorges, J., & Wild, E. (2016). The role of basic need satisfaction for junior academics’ goal conflicts and teaching motivation. Higher Education, 72(2), 175–190. https://doi.org/10.1007/ s10734-015-9944-0
  • Finney, S. J., & DiStefano, C. (2006). Nonnormal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269–314). IAP Information Age Publishing.
  • Gill, J. (2002). Bayesian methods: A social and behavioural sciences approach. CRC Press.
  • González‐Sanmamed, M., Muñoz‐Carril, P. C., & Santos‐Caamaño, F. (2019). Key components of learning ecologies: A Delphi assessment. British Journal of Educational Technology, 50(4), 1–17. https://doi.org/10.1111/bjet.12805
  • González-Sanmamed, M., Sangrà, A., Souto-Seijo, A., & Estévez, I. (2020). Learning ecologies in the digital era: challenges for higher education. Publicaciones, 50(1), 83–102. https://doi.org/10.30827/publicaciones.v50i1.15671
  • Han, J., & Yin, H. (2016). Teacher motivation: Definition, research development and implications for teachers. Cogent Education, 3(1). https:// doi.org/10.1080/2331186X.2016.1217819
  • Hipp, J. R., & Bauer, D. J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11(1), 36–53. https://doi. org/10.1037/1082-989X.11.1.36
  • Jackson, N. (2013). The Concept of Learning Ecologies. En N. Jackson &
  • G. B. Cooper (Eds.), Lifewide Learning Education and Personal Development (pp. 1-21). https://bit.ly/2Bujbi1
  • Lechuga, V. M., & Lechuga, D. C. (2012). Faculty motivation and scholarly work: Self-determination and self-regulation perspectives. Journal of the Professoriate, 6(2), 59-97. https://bit.ly/3djik2o
  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in anormal mixture. Biometrika, 88(3), 767-778. https://doi. org/10.1093/biomet/88.3.767
  • McMullen, J., Van Hoof, J., Degrande, T., Verschaffel, L., & Van Dooren, W. (2018). Profiles of rational number knowledge in Finnish and Flemish students–A multigroup latent class analysis. Learning and Individual Differences, 66, 70-77. https://doi.org/10.1016/j. lindif.2018.02.005
  • Ministerio de Universidades (2019). Sistema Integrado de Información Universitaria (SIIU). Madrid.
  • Monroy, L., Vidal, R., & Saade, A. (2010). Análisis de clases latentes. Una técnica para detectar heterogeneidad en poblaciones. Cuaderno técnico, 2. Centro Nacional de Evaluación para la Educación Superior, A.C.
  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation Study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396
  • Padilla, M.A., & Thompson, J. N. (2016). Burning out faculty at doctoral research universities. Stress and Health, 32(5), 551-558. https://doi.org/10.1002/smi.2661
  • Rao, M. B. (2016). Motivation of teachers in higher education. Journal of Applied Research in Higher Education, 8(4), 469-488
  • Richardson, P. W., Karabenick, S. A., & Watt, H. M. G. (2014). Teacher Motivation: Theory and Practice. Routledge.
  • Rodríguez, S., Núñez, J. C., Valle, A., Blas, R., & Rosario, P. (2009). Auto-eficacia docente, motivación del profesor y estrategias de enseñanza. Escritos de Psicología, 3(1), 1–7. https://bit. ly/2NjVBqP
  • Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54–97.
  • Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi. org/10.1016/j.cedpsych.2020.101860
  • Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications. Pearson Education.
  • Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. https://doi. org/10.1214/aos/1176344136
  • Siemens, G. (2007). Connectivism: creating a learning ecology in distributed environments. En T. Hug (Ed.), Didactics of microlearning: Concepts, discourses and examples (pp. 53–68). Waxmann.
  • Stupnisky, R., BrckaLorenz, A., Yuhas, B., & Guay, F. (2018). Faculty members’ motivation for teaching and best practices: Testing a model based on self- determination theory across institution types. Contemporary Educational Psychology, 53, 15-26. https://doi.org/10.1016/j.cedpsych.2018.01.004
  • Suárez, M. J., & Martín, J. D. (2019). Influencia del perfil sociodemográfico del profesorado universitario sobre la inteligencia emocional y el burnout. Educación XX1, 22(2), 93-117. https:// doi.org/10.5944/educXX1.22514
  • Susacasa, S. (2013). Pedagogía médica: soporte de la formación docente específica para la enseñanza de las Ciencias de la Salud [Tesis doctoral, Universidad Nacional de la Plata]. https://bit. ly/3lYkc3B
  • van Lankveld, T., Schoonenboom, J., Volman, M., Croiset, G., & Beishuizen, J. (2017). Developing a teacher identity in the university context: a systematic review of the literature. Higher Education Research & Development, 36(2), 325–342. https://doi.org/10.1080/07294360.2016.1 208154
  • van den Berg, B.A.M., Bakker, A.B., & ten Cate, T.J. (2013). Key factors in work engagement and job motivation of teaching faculty at a university medical centre. Perspectives on Medical Education, 2, 264–275. https://doi. org/10.1007/s40037-013-0080-1
  • Visser-Wijnveen, G. J., Stes, A., & Van Petegem, P. (2014). Clustering teachers’ motivations for teaching. Teaching in Higher Education, 19(6), 644-656. https:// doi.org/10.1080/13562517.2014.901953
  • Watt, H. M. G., & Richardson, P. W. (2020). Motivation of higher education faculty: (how) it matters! International Journal of Educational Research, 100, 101533. https://doi.org/10.1016/j.ijer.2020.101533
  • Wosnitza, M., Helker, K., & Lohbeck, L. (2014). Teaching goals of early career university teachers in Germany. International Journal of Educational Research, 65, 90–103. ttps://doi.org/10.1016/j.ijer.2013.09.009
  • Zabalza, M. A., Zabalza, M. A., y de Côrte, M. I. (2018). Identidad profesional del profesorado universitario. En I. Cantón & M. Tardiff (Eds.), Identidad profesional docente (pp. 141– 157). Narcea.
Data source: Dialnet