Núm. 9 (2019): PISA como evaluación educativa supranacional: luces y sombras en equidad
Monográfico

PROFESORADO EUROPEO EN PISA 2015: UN ENFOQUE DE MODELACIÓN MULTI NIVEL PARA LA INFRAESTRUCTURA DE LAS TIC Y LA FORMACIÓN DOCENTE

Leo Van Waveren
Universidad Autónoma de Madrid (UAM), España
Publicado diciembre 18, 2019

Palabras clave:

PISA, TICs, Formación docente, modelo multinivel

Resumen

Basándose en las encuestas de PISA 2015 para los maestros y colegios, este articulo pretende investigar las interdependencias e influencias de la formación de los maestros y la infraestructura de TIC en su habilidad y voluntad para emplear los medios modernos en el entorno escolar. Se ha utilizado un enfoque de niveles múltiples para justificar la estructura de datos.

Nuestro análisis demuestra que el entorno de los estudiantes y acciones de TIC ofrecen un poder de predicción limitado en relación con el rendimiento de las pruebas de PISA 2015 de cinco países europeos. Además, los maestros y los directores ven escasez de personal y material educativo bastante diferente en las encuestas del entorno. Sobre la base de esas conclusiones, se puede adaptar unos enfoques nuevos de investigación para entender mejor las influencias que tienen los maestros y las características escolares en resultados del aprendizaje de los estudiantes en las evaluaciones a gran escala. Esto también resalta los problemas potenciales cuando un análisis recurre reuniendo fuentes diferentes para una descripción combinada.

Citas

Araya, R., Gormaz, R., Bahamondez, M., Aguirre, C., Calfucura, P., Jaure, P., & Laborda, C. (2015). Ict Supported Learning Rises Math Achievement in Low Socio Economic Status Schools. In G. Conole, T. Klobučar, C. Rensing, J. Konert, & É. Lavoué (Eds.), Lecture Notes in Computer Science: Vol. 9307. Design for teaching and learning in a networked world: 10th European Conference on Technology Enhanced Learning, EC-TEL 2015, Toledo, Spain, September 15-18, 2015 : proceedings (Vol. 9307, pp. 383–388). Cham, Heidelberg, New York, Dordrecht, London: Springer. https://doi.org/10.1007/978-3-319-24258-3_28

Asparouhov, T., & Muthén, B. (2008). Multilevel Mixture Models. In G. R. Hancock & K. M. Samuelsen (Eds.), CILVR series on latent variable methodology. Advances in latent variable mixture models: The theme for the inaugural conference, held at the University of Maryland on May 18 and 19, 2006, was Mixture Models in Latent Variable Research (pp. 27–52). Charlotte, NC: Information Age Pub.

Blume, B. D., Ford, J. K. [J. Kevin], Baldwin, T. T., & Huang, J. L. (2010). Transfer of Training: A Meta-Analytic Review. Journal of Management, 36(4), 1065–1105. https://doi.org/10.1177/0149206309352880

Bos, W., Eickelmann, B., Gerick, J., Goldhammer, F., Schaumburg, H., Schippert, K., . . . Wendt, H. (Eds.). (2014). ICILS 2013: Computer- und informationsbezogene Kompetenzen von Schülerinnen und Schülern in der 8. Jahrgangsstufe im internationalen Vergleich. Münster: Waxmann.

Burnham, K. P. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644

Chmielewski, A. K., & Savage, C. (2016). Socioeconomic segregation between schools in the US and Latin America, 1970–2012. In G. W. McCarthy, G. K. Ingram, & S. A. Moody (Eds.), Land and the city (pp. 394–423). Cambridge, MA: Lincoln Institute of Land Policy.

Dinis da Costa, P., & Araújo, L. (2016). Digital reading in PISA 2012 and ICT uses: How do VET and general education students perform? EUR, Scientific and technical research series: Vol. 28291. Luxembourg: Publications Office.

European Court of Auditors. (2018). Broadband in the EU Member States: Despite progress, not all the Europe 2020 targets will be met. Special report: No 12, 2018. Luxemburg: Publications Office of the European Union.

Falck, O., Mang, C., & Wößmann, L. (2018). Virtually No Effect? Different Uses of Classroom Computers and their Effect on Student Achievement. Oxford Bulletin of Economics and Statistics, 80(1), 1–38. https://doi.org/10.1111/obes.12192

Garet, M. S., Porter, A. C., Desimone, L., Birman, B. F., & Yoon, K. S. (2001). What Makes Professional Development Effective? Results From a National Sample of Teachers. American Educational Research Journal, 38(4), 915–945. https://doi.org/10.3102/00028312038004915

Gerick, J. (2018). School level characteristics and students’ CIL in Europe – A latent class analysis approach. Computers & Education, 120, 160–171. https://doi.org/10.1016/j.compedu.2018.01.013

Goldhammer, F., Gniewosz, G., & Zylka, J. (2017). Ict Engagement in Learning Environments. In S. Kuger, N. Jude, & D. Kaplan (Eds.), Methodology of Educational Measurement and Assessment. Assessing Contexts of Learning: An International Perspective (Vol. 34, pp. 331–351). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-45357-6_13

Goldstein, H. (2003). Multilevel statistical models (3. ed.). Kendall's library of statistics: Vol. 3. London: Arnold. Retrieved from http://www.loc.gov/catdir/enhancements/fy0615/2003276198-d.html

Goldstein, I. L., & Ford, J. K. [John Kevin]. (2009). Training in organizations: Needs assessment, development, and evaluation (4. ed., [Nachdr.]). Belmont, Calif: Wadsworth/Thomson Learning.

Gómez-Fernández, N., & Mediavilla, M. (2018). Do Information And Communication Technologies (ICT) Improve Educational Outcomes?: Evidence For Spain in PISA 2015 (IEB Working Paper No. 20). https://doi.org/10.13140/RG.2.2.21085.87528

Grossman, R., & Salas, E. (2011). The transfer of training: what really matters. International Journal of Training and Development, 15(2), 103–120. https://doi.org/10.1111/j.1468-2419.2011.00373.x

Güzeller, C. O., & Akın, A. (2014). Relationship between ICT Variables and Mathematics Achievement Based on PISA 2006 Database: International Evidence. Turkish Online Journal of Educational Technology - TOJET, 13(1), 184–192. Retrieved from http://files.eric.ed.gov/fulltext/EJ1018171.pdf

Hanushek, E. A., Ruhose, J., & Wößmann, L. (2016). Knowledge Capital and Aggregate Income Differences: Development Accounting for U.S. States. American Economic Journal: Macroeconomics. https://doi.org/10.3386/w21295

Helmke, A. (2010). Unterrichtsqualität und Lehrerprofessionalität: Diagnose, Evaluation und Verbesserung des Unterrichts ; Franz Emanuel Weinert gewidmet ; [Orientierungsband] (3. Aufl.). [Unterricht verbessern - Schule entwickeln]. Stuttgart: Klett [u.a.].

Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments. 3rd ed. Odessa Fla: Psychological Assessment Resources.

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

Jaensch, V. K., Hirschi, A., & Spurk, D. (2016). Relationships of Vocational Interest Congruence, Differentiation, and Elevation to Career Preparedness Among University Students. Zeitschrift für Arbeits- und Organisationspsychologie A&O, 60(2), 79–89. https://doi.org/10.1026/0932-4089/a000210

Juhaňák, L., Zounek, J., Záleská, K., Bárta, O., & Vlčková, K. (2019). The Relationship Between Students’ ICT Use and Their School Performance: Evidence from PISA 2015 in the Czech Republic. ORBIS SCHOLAE, 12(2), 37–64. https://doi.org/10.14712/23363177.2018.292

Kenny, D. A. (2014). Measuring Model Fit. Retrieved from http://davidakenny.net/cm/fit.htm

Knowles, M. S., Holton, E. F., & Swanson, R. A. (2012). The adult learner: The definitive classic in adult education and human resource development (seventh edition). London, New York: Routledge. https://doi.org/10.4324/9780080964249

Koehler, M. J., Mishra, P., Kereluik, K., Shin, T. S., & Graham, C. R. (2014). The Technological Pedagogical Content Knowledge Framework. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of Research on Educational Communications and Technology (pp. 101–111). New York, NY: Springer New York. https://doi.org/10.1007/978-1-4614-3185-5_9

Maier, A., Nitzschke, A., Nickolaus, R., Schnitzler, A., Velten, S., & Dietzen, A. (2015). Der Einfluss schulischer und betrieblicher Ausbildungsqualität auf die Entwicklung des Fachwissens. In M. Stock, P. Schlögl, K. Schmid, & D. Moser (Eds.), Innovationen in der Berufsbildung: Vol. 9. Kompetent - wofür? Life Skills - Beruflichkeit - Persönlichkeitsbildung: Beiträge zur Berufsbildungsforschung (1st ed., pp. 225–243). Innsbruck: Studien Verlag.

Muthén, B., Muthén, L., Asparouhov, T., & Nguyen, T. (2018). Mplus: Muthén & Muthén.

OECD. (2000). Learning to Bridge the Digital Divide. Education and Skills: OECD. https://doi.org/10.1787/9789264187764-en

OECD. (2016a). PISA 2015 Ergebnisse (Band I): W. Bertelsmann Verlag. https://doi.org/10.1787/19963793

OECD. (2016b). Skills Matter: Further Results From The Survey Of Adult Skills. PIAAC. Paris: OECD Publishing. https://doi.org/10.1787/9789264258051-en

OECD. (2018). Science, Technology and Innovation Outlook 2018: Adapting to Technological and Societal Disruption: OECD. https://doi.org/10.1787/sti_in_outlook-2018-en

Palfrey, J., & Gasser, U. (2008). Born digital: Understanding the first generation of digital natives. New York: Basic Books. Retrieved from http://site.ebrary.com/lib/academiccompletetitles/home.action

Prenzel, M., & Sälzer, C. (2019). Large-scale assessments of educational systems. In R. Becker (Ed.), Research handbooks in sociology. Research handbook on the sociology of education (pp. 536–552). Cheltenham: Edward Elgar. https://doi.org/10.4337/9781788110426.00041

Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modeling. Psychometrika, 69(2), 167–190. https://doi.org/10.1007/BF02295939

Raftery, A. E. (1995). Bayesian Model Selection in Social Research. Sociological Methodology, 25, 111–163. https://doi.org/10.2307/271063

Rammstedt, B. (Ed.). (2013). Erwachsenenbildung 2013/14. Grundlegende Kompetenzen Erwachsener im internationalen Vergleich. Münster: Waxmann Verlag. Retrieved from http://www.content-select.com/index.php?id=bib_view&ean=9783830979999

Raudenbush, S. W., & Bryk, A. S. (2010). Hierarchical linear models: Applications and data analysis methods (2. ed., [Nachdr.]). Advanced quantitative techniques in the social sciences: Vol. 1. Thousand Oaks, Calif.: Sage Publ.

Rosén, M., & Gustafsson, J.‑E. (2016). Is computer availability at home causally related to reading achievement in grade 4? A longitudinal difference in differences approach to IEA data from 1991 to 2006. Large-scale Assessments in Education, 4(1), 130. https://doi.org/10.1186/s40536-016-0020-8

Sälzer, C., & Prenzel, M. (2014). Looking back at five rounds of PISA: Impacts on teaching and learning in Germany. Solsko Polje (The School Field). Evidence from the PISA Study on Educational Quality in Slovenia and Other Countries, XXV, 53–72.

Spokane, A. R., Meir, E. I., & Catalano, M. (2000). Person–Environment Congruence and Holland's Theory: A Review and Reconsideration. Journal of Vocational Behavior, 57(2), 137–187. https://doi.org/10.1006/jvbe.2000.1771

Steffens, K. (2014). ICT Use and Achievement in Three European Countries: What Does PISA Tell Us? European Educational Research Journal, 13(5), 553–562. https://doi.org/10.2304/eerj.2014.13.5.553

Streiner, D. L. (2002). Breaking up is hard to do: The heartbreak of dichotomizing continuous data. Canadian Journal of Psychiatry. Revue Canadienne De Psychiatrie, 47(3), 262–266. https://doi.org/10.1177/070674370204700307

Watermann, R., Maaz, K., Bayer, S., & Roczen, N. (2017). Social Background. In S. Kuger, N. Jude, & D. Kaplan (Eds.), Methodology of Educational Measurement and Assessment. Assessing Contexts of Learning: An International Perspective (Vol. 40, pp. 117–145). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-45357-6_5

Wößmann, L., & Fuchs, T. (2004). Computers and Student Learning: Bivariate and Multivariate Evidence on the Availability and Use of Computers at Home and at School. Retrieved from CESifo Working Paper Series No. 1321 website: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=619101

Wu, M. (2007). ACER ConQuest version 2.0: Generalised item response modelling software. Camberwell, Vic.: ACER Press.

Yalçın, S. (2018). Multilevel Classification of PISA 2015 Research Participant Countries' Literacy and These Classes' Relationship with Information and Communication Technologies. International Journal of Progressive Education, 14(1), 165–176. https://doi.org/10.29329/ijpe.2018.129.12

Youssef, A. B., & Dahmani, M. (2008). The Impact of ICT on Student Performance in Higher Education: Direct Effects, Indirect Effects and Organisational Change. RUSC. Universities and Knowledge Society Journal, 5(1). https://doi.org/10.7238/rusc.v5i1.321

Zhang, D., & Luman, L. (2016). How Does ICT Use Influence Students’ Achievements in Math and Science Over Time?: Evidence from PISA 2000 to 2012. EURASIA Journal of Mathematics, Science & Technology Education, 12(9), 2431–2449. https://doi.org/10.12973/eurasia.2016.1297a