Calibración del resultado de una prueba escrita en estudiantes de ciencias de secundaria: el efecto del sexo

  1. Diego Ardura y Arturo Galán 1
  2. Arturo Galán González 1
  1. 1 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

Revista:
Revista de investigación educativa, RIE

ISSN: 0212-4068 1989-9106

Año de publicación: 2020

Volumen: 38

Número: 2

Páginas: 329-344

Tipo: Artículo

DOI: 10.6018/RIE.384031 DIALNET GOOGLE SCHOLAR lock_openDIGITUM editor

Otras publicaciones en: Revista de investigación educativa, RIE

Objetivos de desarrollo sostenible

Resumen

Durante las últimas décadas se han encontrado importantes diferencias por sexo en la enseñanza y el aprendizaje de las disciplinas científicas. Por otro lado, la autoevaluación por parte de los estudiantes supone un aspecto fundamental en el ciclo de autorregulación del aprendizaje y, por tanto, en su rendimiento. El objetivo de este trabajo es analizar la metacognición de los estudiantes de secundaria y, en particular, el efecto del sexo en las mismas. Para ello se ha medido la calibración del resultado en una prueba escrita de 487 estudiantes. Nuestros análisis muestran que las chicas calibran mejor su nota que los chicos a pesar de que estos últimos muestran más seguridad en sus juicios. Se ha encontrado una tendencia de ambos sexos a la sobreestimación de sus calificaciones en una prueba escrita. Por otro lado, los estudiantes con rendimiento alto son más precisos y tienden a subestimar sus actuaciones. En cambio, los de rendimiento bajo son más imprecisos y tienden a sobreestimar sus calificaciones en la prueba. Aunque este efecto se observa en ambos sexos, su tamaño es superior en el caso de las chicas. En vista de los resultados, los estudiantes de rendimiento alto utilizan con más eficacia la retroalimentación que generan durante la prueba que los de rendimiento bajo. Las diferencias por sexo podrían tener su origen en las diferentes actitudes y motivaciones de los chicos y las chicas hacia la ciencia.

Referencias bibliográficas

  • Abraham, J. y Barker, K. (2015). Exploring gender difference in motivation, engagement and enrolment behavior of senior secondary physics students in New South Wales. Research in Science Education, 45(1), 59-73, doi: 10.1007/s11165-014-9413-2.
  • Acar Ö., Türkmen L., y Bilgin A., (2015). Examination of Gender Differences on Cognitive and Motivational Factors that Influence 8th Graders’ Science Achievement in Turkey. Eurasia Journal of Mathematics Science Technology Education, 11(5), 1027–1040, doi: 10.12973/eurasia.2015.1372a
  • Baars, M., Vink, S., van Gog, T., de Bruin, A., y Paas, F. (2014). Effects of training self-assessment and using assessment standards on retrospective and prospective monitoring of problem solving. Learning and Instruction, 33, 92–107, doi: 10.1016/j.learninstruc.2014.04.004
  • Bol, L., Hacker, D. J., Walck, C. C., y Nunnery, J. A. (2012). The effects of individual or group guidelines on the calibration accuracy and achievement of high school biology students. Contemporary Educational Psychology, 37(4), 280–287, doi: 10.1016/j.cedpsych.2012.02.004
  • Brannick, M. T., Miles, D. E., y Kisamore, J. L. (2005). Calibration between student mastery and self‐efficacy. Studies in Higher Education, 30(4), 473–483, doi: 10.1080/03075070500160244
  • Brown, G. T. L., Andrade, H. L., y Chen, F. (2015). Accuracy in student self-assessment: directions and cautions for research. Assessment in Education: Principles, Policy & Practice, 22(4), 444–457, doi: 10.1080/0969594X.2014.996523
  • Chiu, M. M., y Klassen, R. M. (2010). Relations of mathematics self-concept and its calibration with mathematics achievement: Cultural differences among fifteen-year-olds in 34 countries. Learning and Instruction, 20(1), 2–17, doi: 10.1016/j.learninstruc.2008.11.002
  • de Bruin, A. B. H., Kok, E. M., Lobbestael, J. y de Grip, A. (2017). The impact of an online tool for monitoring and regulating learning at university: overconfidence, learning strategy, and personality. Metacognition and Learning, 12(1), 21–43, doi: 10.1007/s11409-016-9159-5
  • Dent, A. L., y Koenka, A. C. (2016). The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence: A Meta-Analysis. Educational Psychology Review, 28(3), 425–474, doi: 10.1007/s10648-015-9320-8
  • Dunlosky, J., y Metcalfe, J. (2008). Metacognition. Los Angeles, CA: SAGE Publications
  • Dunning, D. (2005). Self-insights: Roadblocks and detours on the path of knowing thyself. New York: Psychology Press.
  • Eddy, S. L., y Brownell, S. E. (2016). Beneath the numbers: A review of gender disparities in undergraduate education across science, technology, engineering, and math disciplines. Physical Review Physics Education Research, 12(2), 020106, doi: 10.1103/PhysRevPhysEducRes.12.020106
  • Erickson, S., y Heit, E. (2015). Metacognition and confidence: comparing math to other academic subjects. Frontiers in Psychology, 6, 742, doi: 10.3389/fpsyg.2015.00742
  • Fischer, F., Schult, J., y Hell, B. (2013). Sex differences in secondary school success: Why female students perform better. European journal of psychology of education, 28(2), 529-543, doi: 10.1007/s10212-012-0127-4
  • Follmer, D. J., y Sperling, R. A. (2016). The mediating role of metacognition in the relationship between executive function and self-regulated learning. British Journal of Educational Psychology, 86(4), 559–575, doi: 10.1111/bjep.12123
  • Glynn, S. M., Brickman, P., Armstrong, N., y Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159–1176, doi: 10.1002/tea.20442
  • Gutiérrez, A. P., y Price, A. F. (2017). Calibration between undergraduate students' prediction of and actual performance: The role of gender and performance attributions. The Journal of Experimental Education, 85(3), 486-500, doi: 10.1080/00220973.2016.1180278
  • Gutierrez, A. P., Schraw, G., Kuch, F., y Richmond, A. S. (2016). A two-process model of metacognitive monitoring: Evidence for general accuracy and error factors. Learning and Instruction, 44, 1–10, doi: 10.1016/j.learninstruc.2016.02.006
  • Hacker, D. J., Bol, L., y Bahbahani, K. (2008). Explaining calibration accuracy in classroom contexts: the effects of incentives, reflection, and explanatory style. Metacognition and Learning, 3(2), 101–121, doi: 10.1007/s11409-008-9021-5
  • Hacker, D. J., Bol, L., Horgan, D. D., y Rakow, E. A. (2000). Test prediction and performance in a classroom context. Journal of Educational Psychology, 92(1), 160–170, doi: 10.1037/0022-0663.92.1.160
  • Hacker, D. J., Bol, L., y Keener, M. C. (2008). Metacognition in education: A focus on calibration. In J. Dunlosky y R. A. Bjork (Eds.), Handbook of metamemory and memory (p. 429455). New York: Taylor & Francis Group.
  • Hawker, M. J., Dysleski, L., y Rickey, D. (2016). Investigating General Chemistry Students’ Metacognitive Monitoring of Their Exam Performance by Measuring Postdiction Accuracies over Time. Journal of Chemical Education, 93(5), 832–840, doi: 10.1021/acs.jchemed.5b00705
  • Jacobs, J.E. (2005). Twenty-five years of research on gender and ethnic differences in math and science career choices: What havewe learned? En J.E. Jacobs & S.D. Simpkins (Eds.), New Directions for Child and Adolescent Development, 110, 85–94. doi: 10.1002/cd.151
  • Karatjas, A. G. (2013). Comparing College Students’ Self-Assessment of Knowledge in Organic Chemistry to Their Actual Performance. Journal of Chemical Education, 90(8), 1096–1099, doi: 10.1021/ed400037p
  • Karatjas, A. G. (2014). Use of Student Self-Assessment of Exams To Investigate Student Learning in Organic Chemistry Classes. En Kendhammer, L. K. y Murphy, K. L. (Eds.) Innovative Uses of Assessments for Teaching and Research (pp. 133–143). American Chemical Society, doi: 10.1021/bk-2014-1182.ch008
  • Karatjas, A. G., y Webb, J. (2015). The Role of Gender in Grade Perception in Chemistry Courses. Journal of College Science Teaching, 45(2), 30–35, doi: 10.20429/ijsotl.2017.110214
  • Kruger, J., y Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134.
  • Lindsey, B. A., y Nagel, M. L. (2015). Do students know what they know? Exploring the accuracy of students’ self-assessments. Physical Review Special Topics - Physics Education Research, 11(2), 20103, doi: 10.1103/PhysRevSTPER.11.020103
  • Mujtaba, T., y Reiss, M. J. (2013). What Sort of Girl Wants to Study Physics After the Age of 16? Findings from a Large-scale UK Survey. International Journal of Science Education, 35(17), 2979–2998, doi: 10.1080/09500693.2012.681076
  • Nietfeld, J. L., Shores, L. R., y Hoffmann, K. F. (2014). Self-regulation and gender within a game-based learning environment. Journal of Educational Psychology, 106(4), 961–973, doi: 10.1037/a0037116
  • Palmer T.-A., Burke P. F., y Aubusson P. (2017). Why school students choose and reject science: a study of the factors that students consider when selecting subjects. Int. J. Sci. Educ., 39(6), 645–662, doi: 10.1080/09500693.2017.1299949
  • Pirmohamed, S., Debowska, A., y Boduszek, D. (2017). Gender differences in the correlates of academic achievement among university students. Journal of Applied Research in Higher Education, 9(2), 313-324. doi: 10.1108/JARHE-03-2016-0015
  • Potvin P., y Hasni A., (2014). Interest, motivation and attitude towards science and technology at K-12 levels: a systematic review of 12 years of educational research. Studies in Science Education, 50(1), 85–129, doi: 10.1080/03057267.2014.881626
  • Schraw, G., Potenza, M. T., y Nebelsick-Gullet, L. (1993). Constraints on the calibration of performance. Contemporary Educational Psychology, 18(4), 455–463, doi: 10.1006/ceps.1993.1034
  • Sharma, M. D., y Bewes, J. (2011). Self-monitoring: Confidence, academic achievement and gender differences in Physics. Journal of Learning Design, 4(3), 1–13, doi: 10.5204/jld.v4i3.76
  • Schumm, M. F., y Bogner, F. X. (2016). Measuring adolescent science motivation. International Journal of Science Education, 38(3), 434–449, doi: 10.1080/09500693.2016.1147659
  • Vázquez Alonso, Á., y Manassero Más, M. (2015). La elección de estudios superiores científico-técnicos: análisis de algunos factores determinantes en seis países. Revista Eureka sobre enseñanza y divulgación de las ciencias, 12(2), 264-277.
  • Volz-Sidiropoulou, E., y Gauggel, S. (2012). Do subjective measures of attention and memory predict actual performance? Metacognition in older couples. Psychology and Aging, 27(2), 440–450, doi: 10.1037/a0025384
  • Zamora Á., y Ardura D., (2014). ¿En qué medida utilizan los estudiantes de Física de Bachillerato sus propios errores para aprender? Una experiencia de autorregulación en el aula de secundaria. Enseñanza de las ciencias, 32(2), 253–268, doi: 10.5565/rev/ensciencias.1067
  • Zamora Á., Suárez J. M., y Ardura D., (2018). Error detection and self-assessment as mechanisms to promote self-regulation of learning among secondary education students. Journal of Educational Research, 111(2), 175–185, doi: 10.1080/00220671.2016.1225657