Validation of the Activities’ Scale in Higher Education Students’ Personal Learning Environments

  1. José-Antonio García-Martínez 1
  2. Mercedes González-Sanmamed 2
  3. Pablo-César Muñoz-Carril 3
  1. 1 Universidad Nacional (Costa Rica)
  2. 2 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  3. 3 Universidade de Santiago de Compostela
    info

    Universidade de Santiago de Compostela

    Santiago de Compostela, España

    ROR https://ror.org/030eybx10

Mostrar afiliacións +
Revista:
Psicothema

ISSN: 0214-9915,  1886-144X

Ano de publicación: 2021

Volume: 33

Número: 2

Páxinas: 320-327

Tipo: Artigo

DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: Psicothema

Resumo

Antecedentes: los entornos personales de aprendizaje se definen como el entramado de herramientas, actividades y conexiones que cada persona utiliza para su aprendizaje. Los estudios sobre el tema han ido en aumento, sin embargo, son todavía escasos los instrumentos de medición al respecto. El objetivo de este trabajo es construir y validar una escala para evaluar las actividades que integran los Entornos Personales de Aprendizaje. Método: la muestra estaba formada por 1.187 estudiantes universitarios de último año de carrera. Un 64% eran mujeres y un 36% hombres, con una edad media de 24 años y una desviación típica de 4.21. Resultados: la escala queda formada por 27 ítems tipo Likert respondiendo a tres factores de acuerdo con el constructo teórico revisado, obteniendo coeficientes elevados en las pruebas de consistencia interna. Conclusiones: los análisis realizados muestran un instrumento válido y con propiedades psicométricas sólidas. Concretamente, los resultados arrojan una adecuada validez de contenido. Los análisis factoriales exploratorio y confirmatorio indican una pertinente validación de constructo, existiendo coherencia entre el modelo teórico y factorial.

€ Ver financiamento

Información de financiamento

This article was produced within the framework of the research project entitled: “Ecologías de aprendizaje en la era digital: nuevas oportunidades para la formación del profesorado de educación secundaria” (ECO4LEARN-SE), partly financed by the Ministerio de Ciencia, Innovación y Universidades (Referencia RTI2018-095690-B-I00).

Financiadores

Referencias bibliográficas

  • Adell, J., & Castañeda, L. (2010). The Personal Learning Environments (PLEs): A new way of understanding learning. In Roig Vila, R., & Fiorucci, M. (Eds.), Keys for the Research in Educational Innovation and Quality. The Integration of the Information Technologies, the Communication, and the Multiculturalism in the Classroom. Roma TRE Universita degli studi.
  • Alharbi, H. A. (2018). Readiness for self-directed learning: How bridging and traditional nursing students differs? Nurse Education Today, 61, 231-234. https://doi.org/10.1016/j.nedt.2017.12.002
  • Arnal, J., Del Rincón, D., & Latorre, A. (1992). Investigación educativa. Fundamentos y metodología [Educational research. Fundamentals and methodology]. Labor.
  • Attwell, G., Castañeda, L., & Buchem, I. (2013). Special issue from the personal learning environments 2011 conference. International Journal of Virtual and Personal Learning Environments, 4(4), 1-4.
  • Bentler, P. M. (1995). EQS structural equations program manual. BMDP Statistical Software.
  • Boza, A., & Conde, S. (2015). Web 2.0 in higher education: Attitude, training, use, impact, challenges and tools of web 2.0. Digital Education Review, 28, 45-58.
  • Castañeda, L., & Adell, J. (Eds.) (2013). Entornos personales de aprendizaje: claves para el ecosistema educativo en red [Personal learning environments: Keys to the online educational ecosystem]. Marfil.
  • Chaves-Barboza, E., & Rodríguez-Miranda, L. (2017). Reliability and validity analysis of a questionnaire on personal learning environments (PLE). Ensayos Pedagógicos, 13(1), 71-106. https://doi.org/10.15359/rep.13-1.4
  • Coiro, J., & Dobler, E. (2007). Exploring the online reading comprehension strategies used by sixth-grade skilled readers to search for and locate information on the internet. Reading Research Quarterly, 42(2), 214-257. http://dx.doi.org/10.1598/RRQ.42.2.2
  • Dabbagh, N., & Fake, H. (2017). College students’ perceptions of personal learning environments through the lens of digital tools, processes and spaces. Journal of New Approaches in Educational Research, 6(1), 28-36. https://doi.org/10.7821/naer.2017.1.215
  • Drexler, W. (2010). The networked student model for construction of personal learning environments: Balancing teacher control and student autonomy. Australasian Journal of Educational Technology, 26(3), 369-385. https://doi.org/10.14742/ajet.1081
  • Fiedler, S. H. D., & Väljataga, T. (2013). Personal learning environments: A conceptual landscape revisited. eLearning Papers, 35, 1-16.
  • Finney, S.J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G.R. Hancock & R.O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269-314). Information Age Publishing.
  • García-Martínez, J.A., & González-Sanmamed, M. (2017). Personal Learning Environments of Costa Rican Education Students: Analysis of Information Search Tools. Revista de Investigación Educativa,35(2), 389-407. https://doi.org/10.6018/rie.35.2.253101
  • García-Martínez, J.A., & González-Sanmamed, M. (2019). How do Costa Rican education students generate and manage content: A contribution to the study of their personal learning environment. Digital Education Review, 36, 15-35.
  • García-Martínez, J.A., Rosa-Napal, F.C., Romero-Tabeayo, I., López-Calvo, S., & Fuentes-Abeledo, E.J. (2020). Digital Tools and Personal Learning Environments: An Analysis in Higher Education. Sustainability, 12(19), 8180. https://doi.org/10.3390/su12198180
  • García-Valcárcel, A., & Hernández, A. (2013). Recursos tecnológicos para la enseñanza e innovación educativa [Technological resources for teaching and educational innovation]. Síntesis S.A.
  • Hair, J., Black, W. C., Babin, B., Anderson, R. E., & Tatham, R. (2005). Multivariate data analyses (6ª ed.). Prentice-Hall.
  • Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fi t indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. https://doi.org/10.1080/10705519909540118
  • Izquierdo, I., Olea, J., & Abad, F. J. (2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26(3), 395-400. https://doi.org/10.7334/psicothema2013.349
  • Keereerat, C., Na-Songkhla, J., & Sujiva, S. (2019). A study of creating personal learning environments by students of the faculty of education majoring in computer education. Journal of Education Studies, 47(4), 176-196.
  • Kline, R. B. (2011). Principles and practice of structural equation modelling (3rd ed.). Guilford Press.
  • Kompen, R., Edirisingha, P., Canaleta, X., Alsina, M., & Monguet, J. M. (2019). Personal learning environments based on Web 2.0 services in higher education. Telematics and Informatics, 38, 194-206. https://doi.org/10.1016/j.tele.2018.10.003
  • Kop, R. (2011). The Challenges to Connectivist learning on open online networks: Learning experiences during a Massive Open Online Course. The International Review of Research in Open and Distributed Learning, 12(3), 19-38. https://doi.org/10.19173/irrodl.v12i3.882
  • Kühn, C. (2017). Are students ready to (re)-design their personal learning environment? The case of the e-Dynamic. Space, Journal of New Approaches in Educational Research, 6(1), 11-20. https://doi.org/10.7821/naer.2017.1.185
  • Lorenzo-Seva, U., & Ferrando, P.J. (2006). FACTOR: A computer program to fi t the exploratory factor analysis model. Behavioral ResearchMethods, Instruments and Computers, 38(1), 88-91.
  • Marín, V., Negre, F., & Pérez, A. (2014). Construction of the foundations of the PLE and PLN for collaborative learning. Comunicar: Revista Científica Iberoamericana de Comunicación y Educación, 42(21), 35-43. https://doi.org/10.3916/C42-2014-03
  • Marsh, H. W., Balla, J. R., & Hau, K. T. (1996). An evaluation of incremental fi t indices: A clarif cation of mathematical and empirical processes. En G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling techniques (pp. 115-353). Erlbaum.
  • McMillan, J.H., & Schumacher, S. (2005).Investigación educativa. Una introducción conceptual [Education Research. A conceptual introduction]. Editorial Pearson Educación, SA.
  • Meza, J.M., Morales, M. E., & Flores, R. D. C. (2016). Individual variables related to instruction in the use of Personal Learning Environments. Educación, 25(48), 87-106.
  • Muñiz, J. (2003). Teoría clásica de los tests [Classical Test Theory]. Pirámide.
  • Muñiz, J., & Fonseca-Pedrero, E. (2019). Ten steps for test development. Psicothema, 31(1), 7-16. https://10.7334/psicothema2018.291
  • Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
  • Osterlind, S.J. (1992). Constructing test items: Multiple-choice, constructed-response, performance, and other formats. Kluwer Academic Publishers.
  • Prendes-Espinosa, M.P., Castañeda-Quintero, L., Solano-Fernández, I.M., Roig-Vila, R., Aguilar-Perera, M.V., & Serrano-Sánchez, J.L. (2016). Validation of a questionnaire on work and learning habits for future professionals: Exploring personal learning environments, Relieve, 2(2),1-46. http://dx.doi.org/10.7203/relieve.22.2.7228
  • Rahimi, E., Berg, J., & Veen, W. (2015). Facilitating student-driven constructing of learning environments using Web 2.0 personal learning environments. Computers & Education, 81, 235-246. https://doi.org/10.1016/j.compedu.2014.10.012
  • Ramírez-Mera, U., & Tur, G. (2019). Security and reliability in the information management in personal learning environments (PLE) in higher education. Edutec. Revista Electrónica De Tecnología Educativa, 70, 18-33. https://doi.org/10.21556/edutec.2019.70.1435
  • Şahin, S., & Uluyol, Ç. (2016). Preservice teachers’ perception and use of personal learning environments (PLEs). The International Review of Research in Open and Distributed Learning, 17(2), 141-161. https://doi.org/10.19173/irrodl.v17i2.2284
  • Salleh, U. K. M., Zulnaidi, H., & Rahim, S. S. A. (2019). Factors affecting university students’lifelong learning in Indonesia . MOJEM: Malaysian Online Journal of Educational Management, 8(1), 82-97. https://mojem.um.edu.my/article/view/21366
  • Sanduvete-Chaves, S., Chacón-Moscoso, S., Sánchez-Martín, M., & Pérez-Gil, J. A. (2013). The revised Osterlind Index . A comparative analysis in content validity studies. Acción Psicológica, 10(2), 3-20. http://dx.doi.org/10.5944/ap.10.2.11821
  • Sireci, S.G. (1998). Gathering and analyzing content validity data. Educational Assessment, 5(4), 299-321. https://doi.org/10.1207/s15326977ea0504_2
  • Vigil-Colet, A., Navarro-González, D., & Morales-Vives, F. (2020). To reverse or to not reverse Likert-type items: That is the question. Psicothema, 32(1), 108-114. https://doi.org/10.7334/psicothema2019.286
Fonte de datos: Dialnet