Posicionamiento del alumnado de Educación Primaria y Secundaria ante la personalización del aprendizajeconstrucción y validación de una escala
- César Coll 1
- Jaime Fauré 1
- Rubén Arriazu 2
-
1
Universitat de Barcelona
info
-
2
Universidad de Extremadura
info
ISSN: 0034-8082
Año de publicación: 2022
Título del ejemplar: Explorando lo común y lo público en las prácticas de enseñanza
Número: 395
Páginas: 265-290
Tipo: Artículo
Otras publicaciones en: Revista de educación
Resumen
In this work we present the formulation and psychometric validation of a scale for the exploration of primary and secondary students’ views of school learning personalisation strategies (EPAE-A from the Spanish Escala de Personalisación del Aprendizaje Escolar – Alumnado, School Learning Personalisation Scale – Students). The instrument was developed as part of a broader research project which analyses educational innovation involving personalisation of learning. The starting point for the formulation of the scale was to establish a typology of teaching practices and strategies which, according to a review of the relevant literature, promote school learning that has personal meaning and significance for the students. In order to validate the EPAE-A, two preliminary pilot studies were conducted, the first involving 507 students and the second 1,411 students. The process enabled us to reduce the initial set of items to a total of 34. These were grouped into two sub-scales: one of frequency (10 items) and the other of agreement (24 items). Factor analysis revealed a four-factor structure of the agreement sub-scale corresponding to four dimensions of learning personalisation: 1) learner’s control and decision-making regarding the learning process; 2) experiential and emotional basis; 3) connections between learning experiences; and 4) reflection upon oneself as a learner and upon the learning process itself. A unidimensional structure was identified for the frequency subscale, grouping items according to specific teacher and student actions related to learning personalisation strategies. In order to obtain evidence of the validity and reliability of the scale, the final version was applied to a sample of 4,909 students aged between 10 and 18 years from educational institutions located in Catalonia, Extremadura and Madrid, Spain. The results provide strong evidence of the internal structural validity and reliability of the scale.
Referencias bibliográficas
- Aiken, L. R. (2003). Tests psicológicos y evaluación. Pearson Educación. American Psychological Association (2015). Top 20 principles from psychology for preK-12 teaching and learning. http://www.apa.org/ ed/schools/teaching-learning/top-twenty-principles.pdf]
- American Educational Research Association [AERA], American Psychological Association [APA] & National Council on Measurement in Education [NCME]. (1999). Standards for Educational and Psychological Testing. American Psychological Association.
- Asparouhov, T., & Muthén, B. (2010). Computing the strictly positive Satorra-Bentler chi-square test in Mplus. Mplus Web Notes, 1-12.
- Australian Curriculum, Assessment and Reporting Authority (ACARA) (2013). Student Diversity and the Australian Curriculum. Advice for principals, schools and teachers. Sydney: ACARA. https://docplayer. net/5749166-Student-diversity-and-the-australian-curriculum.html.
- Bandalos, D. L., & Finney, S. J. (2018). Factor analysis: Exploratory and confirmatory. En The reviewer’s guide to quantitative methods in the social sciences (pp. 98-122). Routledge.
- Batista-Foguet, J. M., Coenders, G., & Alonso, J. (2004). Análisis factorial confirmatorio. Su utilidad en la validación de cuestionarios relacionados con la salud. Medicina clínica, 122(1), 21-27.
- Boateng, G.O., Neilands, T.B., Frongillo, E.A., Melgar-Quiñonez, H.R. & Sera L. Young, S.L. (2018). Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer. Frontiers in Public Health, 6:149, 1-18. https://doi.org/10.3389/fpubh.2018.00149
- Bray, B., & McClaskey, K. (2013). A step-by-step guide to personalize learning. Learning & Leading with Technology, 40(7), 12–19.
- Bray, B., & McClaskey, K. (2015). Make learning personal: The what, who, WOW, where, and why. Corwin Press.
- Carmines, E.G. & Zeller, R.A. (1988). Reliability and Validity Assessment. Sage.
- Cohen, R. J., Swerdlik, M. E., & Phillips, S. M. (1996). Psychological testing and assessment: An introduction to tests and measurement. Mayfield Publishing Co.
- Chrysafiadi, K., Virvou, M. & Sakkopoulos, E. (2020). Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment. En M. Virvou, E. Alepis, G.A. Tsihrintzis & L.C. (Eds.), Machine Learning Paradigms. Advances in Learning Analytics (pp. 205-223). Springer.
- DeMink-Carthew, J. & Netcoh, S. (2019) Mixed Feelings about Choice: Exploring Variation in Middle School Student Experiences with Making Choices in a Personalized Learning Project. RMLE Online, 42(10), 1-20. https://doi.org/10.1080/19404476.2019.1693480.
- Department for Education and Skills (DfES). (2004) A national conversation about personalised learning. DfES. https://www.education.gov.uk/ publications/eOrderingDownload/DfES%200919%20200MIG186.pdf.
- Elosúa Oliden, P., & Zumbo, B. D. (2008). Coeficientes de fiabilidad para escalas de respuesta categórica ordenada. Psicothema, 20(4), 896-901.
- FitzGerald, E., Jones, A.; Kucirkova, N. & Scanlon, E. (2018). A literature synthesis of personalised technology-enhanced learning: what works and why. Research in Learning Technology, 26: 2095, 1-16. http:// dx.doi.org/10.25304/rlt.v26.2095
- Grant, P. & Basye, D. (2014). Personalised Learning. A Guide for Engaging Students with Technology. International Society for Technology in Education -ISTE.
- Hammonds, V. & Moyer, J. (2018). From Vision to Reality. Personalized, Competency-Based Education for All Kids. https://knowledgeworks. org/resources/vision-reality-personalized-cbe/.
- Holmes, W., Anastopoulou, S., Schaumburg, H. and Mavrikis, M. (2018). Technology-enhanced Personalised Learning: Untangling the Evidence. Open Research Online. The Open University. http://www. studie-personalisiertes-lernen.de/en/.
- Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve?. International Journal of Human-Computer Interaction, 32(1), 51-62. https://doi.org/10.1080/10447318.2015.108 7664
- Izquierdo, I., Olea, J., & Abad, F. J. (2014). El análisis factorial exploratorio en estudios de validación: usos y recomendaciones. Psicothema, 26(3), 395-400. https://doi.org/10.7334/psicothema2013.349
- Johnson, L., Adams Becker, S., Estrada V., Freeman, A., Kampylis, P., Vuorikari, R. & Punie, Y. (2014). NMC Horizon Report Europe - 2014 Schools Edition. The New Media Consortium. http:// publications.europa.eu/resource/cellar/1eda751c-a440-4b5e-8b53- 04243d3ff8b3.0001.02/DOC_1
- Jonnaert, Ph. (2019). Action située – Agir compétent. BACSE International. Paper online: http://bacseinternational.com.
- Jones, M. & McLean, K. (2018). Chapter 2. Personalising Learning: An Overview. En M. Jones & K, McLean, Personalising Learning in Teacher Education (pp. 9-23). Springer Nature Singapore Pte Ltd. https://doi. org/10.1007/978-981-10-7930-6
- Jones, A., Scanlon, E., Gaved, M., Blake, C., Collins, T., Clough, G., Kerawalla, L., Littleton, K., Mulholland, P., Petrou, M. & Twiner, A. (2013). Challenges in personalisation: supporting mobile science inquiry learning across contexts. Research and Practice in Technology Enhanced Learning, 8(1), 21–42.
- Kane, M. (2012). All validity is construct validity. Or is it?. Measurement: Interdisciplinary Research & Perspective, 10(1-2), 66-70. https://doi.or g/10.1080/15366367.2012.681977.
- Kane, M. T. (2016). Explicating validity. Assessment in Education: Principles, Policy & Practice, 23(2), 198-211.
- Kabassi, K. & Alepis, E. (2020). Learning Analytics in Distance and Mobile Learning for Designing Personalised Software. En M. Virvou, E. Alepis, G.A. Tsihrintzis & L.C. (Eds.), Machine Learning Paradigms. Advances in Learning Analytics (pp. 185-203). Springer.
- Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486-507. https://doi.org/10.1177/0049124114543236.
- Lee, D. (2014). How to Personalize Learning in K-12 Schools: Five Essential Design Features. Educational Technology, 54(3),12-17.
- Levine, E. & Patrick, S. (2019). What is Competency-Based Education. An updated definition. Aurora Institute. https://aurora-institute.org/ resource/what-is-competency-based-education-an-updated-definition/
- Manzano, P. A. & Zamora M., Salvador (2009). Sistema de Ecuaciones Estructurales: Una Herramienta de Investigación. Cuaderno Técnico 4. Centro Nacional de Evaluación para la Educación Superior, A.C. https://docplayer.es/42086072-Sistema-de-ecuaciones-estructuralesuna-herramienta-de-investigacion.html
- Marope, M. (2017a). Reconceptualizing and Repositioning Curriculum in the 21st Century. A Global Paradigm Shift. IBE-UNESCO. http://bit. ly/2wIkJm5
- Marope, M. (2017b). Future Competences and the Future of Curriculum. A global reference for Curricula Transformation. IBE-UNESCO. http://www.ibe.unesco.org/es/news/document-future-competencesand-future-curriculum
- McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.
- National Academies of Sciences, Engineering, and Medicine. (2018). How People Learn II: Learners, Contexts, and Cultures. The National Academies Press. https://doi.org/10.17226/24783
- Netcoh, S. (2017). Students’ Experiences with Personalized Learning: An Examination Using Self-Determination Theory. University of Vermont. Graduate College Dissertations and Theses. https://scholarworks. uvm.edu/graddis/738
- Organisation for Economic Co-operation and Development (OECD) (2006). Schooling for Tomorrow: Personalising Education. OECD Publishing.
- Organisation for Economic Co-operation and Development (OECD) (2017). The principles of learning to design learning environments. En The OECD Handbook for Innovative Learning Environments (chapter 1, pp. 21-40). OECD Publishing.
- Olofson, M.W. & Downes, J. (2018). An Instrument to Measure Teacher Practices to Support Personalized Learning in the Middle Grades. RMLE Online, 41(7), 1-21, https://doi.org/10.1080/19404476.2018.14 93858
- Perrenoud, Ph. (2000). L’école saisie par les compétences. En: Ch. Bosman, F.-M. Gerard & X. Roegiers (Eds.), Quel avenir pour les compétences? (pp. 21-41). De Boeck & Larcier.
- Prain, V., Cox, P., Deed, C., Dorman, J., Edwards, D., Farrelly, C., Keeffe, M., Lovejoy, V., Mow, L., Sellings, P., Waldrip, B. & Yager, Z. (2013). Personalised learning: lessons to be learnt. British Educational Research Journal, 39(4), 654–676. http://dx.doi.org/10.1080/014119 26.2012.669747
- Prieto, G., & Delgado, A. R. (2010). Fiabilidad y validez. Papeles del psicólogo, 31(1), 67-74.
- Sawyer, R.K. (2014). Introduction: The New Science of Learning. En R. K. Sawyer (Ed.), The Cambridge Handbook of Learning Sciences. Second Edition (pp. 1-18). Cambridge University Press.
- Schmid, R. & Petko, D. (2019). Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students? Computers & Education, 136, 75–86. https://doi.org/10.1016/j.compedu.2019.03.006
- Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality Assessment of Ordered Polytomous Items with Parallel Analysis. Psychological Methods, 16, 209-220. https://doi.org/10.1037/a0023353
- Underwood, J., Baguely, Th., Banyard, Ph., Dillon, G., Farrington-Flint, L., Hayes, M., Hick, P., LeGeyt, G., Murphy, J., Selwood, I. & Wright, M. (2009). Personalising Learning. BECTA. https://webarchive. nationalarchives.gov.uk/20110107182133/http://research.becta.org. uk/index.php?section=rh&&catcode=_re_rp_02&rid=14546
- Underwood , J.& Banyard , Ph. (2008). Managers’, teachers’ and learners’ perceptions of personalised learning: evidence from Impact 2007. Technology, Pedagogy and Education, 17(3), 233-246. https://doi. org/10.1080/14759390802383850
- UNESCO-IBE (2017). Training Tools for Curriculum Development: Personalized Learning. IBE-UNESCO.
- US Department of Education (2010). Transforming American Education. Learning Powered by Technology. National Education Technology Plan 2010. Department of Education. Education Publications Center. https://bit.ly/3arpHCO
- Waldrip, B., Cox, P., Deed, C., Dorman, J., Edwards, D., Farrelly, C., et al. (2014). Student perceptions of personalised learning: Validation and development of questionnaire with regional secondary students. Learning Environments Research, 17(3), 355–370. https://doi. org/10.1007/s10984-014-9163-0
- Waldrip, B., Yu, J.J. & Prain, V. (2016). Validation of a model of personalized learning. Learning Environments Research, 19, 169-180. https://doi. org/10.1007/s10984-016-9204-y
- Watson, W., & Watson, S. L. (2017). Principles for personalized instruction. En C. Reigeluth, B. J. Beatty, & R. D. Myers (Eds.). Instructional-design theories and models, volume IV: The learner-centered paradigm of education (pp. 87–108). Routledge.
- Zumbo, B. D., & Rupp, A. A. (2004). Responsible modeling of measurement data for appropriate inferences. SAGE.