Redes de aprendizaje personalizadas en contextos universitarios de aprendizaje semipresencial

Personalised learning networks in the university blended learning context

 Feifei Han, Brisbane (Australia)

 Robert Ellis, Brisbane (Australia)

https://doi.org/10.3916/C62-2020-02

Palabras clave

Perspectiva ecológica, red de aprendizaje personalizada, interrelación, dimensión cognitiva, dimensión social, dimensión material, experiencia de aprendizaje semipresencial, estudiantes universitarios

Resumen

En la Educación Superior, pocos estudios han investigado simultáneamente las dimensiones cognitivas, sociales y materiales de una misma población. Desde una perspectiva ecológica del aprendizaje, este estudio examina la interrelación entre elementos clave a partir de estas dimensiones en las redes personalizadas de 365 estudiantes. Los datos procedentes de cuestionarios, análisis de aprendizaje y calificaciones del curso permiten considerar estos aspectos en la experiencia de aprendizaje y en el rendimiento académico. Los participantes registraron niveles cualitativamente dispares en el nivel de implicación en el curso, oscilando de un enfoque orientado a la comprensión a enfoques basados en la reproducción de contenidos, lo que, junto a sus opciones de colaboración, generó cinco patrones distintos. Los resultados revelaron que una orientación más comprensiva y una cooperación con estudiantes de orientaciones similares tiende a asociarse con mejores rendimientos en el aprendizaje semipresencial. Sus redes personalizadas se caracterizaron por enfoques más profundos hacia el aprendizaje presencial y virtual; percepciones positivas hacia la integración de ambos contextos; el diseño del curso, por la forma y modo de colaboración; y por una mayor implicación en las actividades en línea. El estudio tuvo implicaciones significativas de aplicación en el desarrollo teórico de la investigación en la ecología del aprendizaje, así como en la forma de guiar el diseño del currículum, la práctica docente y el aprendizaje.

Referencias

Barnett, R. (2018). The ecological university: A feasible utopia. London: Routledge. https://doi.org/10.4324/9781315194899-1

Link DOI | Link Google Scholar

Barron, B. (2004). Learning ecologies for technological fluency: Gender and experience differences. Journal of Educational Computing Research, 31(1), 1-36. https://doi.org/10.2190/1n20-vv12-4rb5-33va

Link DOI | Link Google Scholar

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

Link DOI | Link Google Scholar

Barron, B., Wise, S., & Martin, C. (2013). Creating within and across life spaces: The role of a computer clubhouse in a child’s learning ecology. In B. Bevan, P. Bell, R. Stevens & A. Razfar (Eds.), LOST Opportunities (pp. 99-118). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-4304-5_8

Link DOI | Link Google Scholar

Biggs, J., Kember, D., & Leung, D. (2001). The revised two-factor study process questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71, 133-149. https://doi.org/10.1348/000709901158433

Link DOI | Link Google Scholar

Bliuc, A.M, Ellis, R., Goodyear, P., & Piggott, L. (2010). Learning through face-to-face and online discussions: Associations between students’ conceptions, approaches and academic performance in political science. British Journal of Educational Technology, 41(3), 512-524. https://doi.org/10.1111/j.1467-8535.2009.00966.x

Link DOI | Link Google Scholar

Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555-564. https://doi.org/10.1016/j.socnet.2007.04.002

Link DOI | Link Google Scholar

Brewe, E., Kramer, L., & Sawtelle, V. (2012). Investigating student communities with network analysis of interactions in a physics learning center. Physical Review Special Topics-PER, 8, 010101. https://doi.org/10.1103/PhysRevSTPER.8.010101

Link DOI | Link Google Scholar

Cadima, R., Ojeda, J., & Monguet, M. (2012). Social networks and performance in distributed learning communities. Educational Technology and Society, 15(4), 296-304. https://bit.ly/2ZTD616

Link Google Scholar

Cope, B., & Kalantzis, M. (2017). E-learning ecologies: Principles for new learning and assessment. New York: Routledge. https://doi.org/10.4324/9781315639215

Link DOI | Link Google Scholar

Crawford, K., Gordon, S., Nicholas, J., & Prosser, M. (1998). Qualitatively different experiences of learning mathematics at university. Learning and Instruction, 8(5), 455-468. https://doi.org/10.1016/S0959-4752(98)00005-X

Link DOI | Link Google Scholar

De-Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek (2nd ed.). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511996368

Link DOI | Link Google Scholar

Ellis, R., & Bliuc, A. (2016). An exploration into first-year university students’ approaches to inquiry and online learning technologies in blended environments. British Journal of Educational Technology, 47(5), 970-980. https://doi.org/10.1111/bjet.12385

Link DOI | Link Google Scholar

Ellis, R., & Goodyear, P. (2019). The education ecology of universities: Integrating learning, strategy and the academy. London: Routledge. https://doi.org/10.4324/9781351135863

Link DOI | Link Google Scholar

Ellis, R., Bliuc, A., & Goodyear, P. (2012). Student experiences of engaged enquiry in pharmacy education: Digital natives or something else? Higher Education, 64(5), 609-626. https://doi.org/10.1007/s10734-012-9515-6

Link DOI | Link Google Scholar

Ellis, R., Pardo, A., & Han, F. (2016). Quality in blended learning environments – significant differences in how students approach learning collaborations. Computers & Education, 102, 90-102. https://doi.org/10.1016/j.compedu.2016.07.006

Link DOI | Link Google Scholar

Entwistle, N., & Ramsden, P. (2015). Understanding student learning. London: Routledge. https://doi.org/10.1016/b978-0-12-805359-1.00012-7

Link DOI | Link Google Scholar

Fenwick, T. (2014). Sociomateriality in medical practice and learning: Attuning to what matters. Medical Education, 48(1), 44-52. https://doi.org/10.1111/medu.12295

Link DOI | Link Google Scholar

Fenwick, T., & Landri, P. (2012). Materialities, textures and pedagogies: Socio-material assemblages in education. Pedagogy, Culture & Society, 20(1), 1-7. https://doi.org/10.1080/14681366.2012.649421

Link DOI | Link Google Scholar

Fenwick, T., Nerland, M., & Jensen, K. (2012). Sociomaterial approaches to conceptualizing professional learning and practice. Journal of Education and Work, 25(1), 1-13. https://doi.org/10.1080/13639080.2012.644901

Link DOI | Link Google Scholar

Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35-41. https://doi.org/10.2307/3033543

Link DOI | Link Google Scholar

Han, F., & Ellis, R. (2019a). Initial development and validation of the perceptions of the blended learning environment questionnaire. Journal of Psychoeducational Assessment. https://doi.org/10.1177/0734282919834091

Link DOI | Link Google Scholar

Han, F., & Ellis, R. (2019b). Identifying consistent patterns of quality learning discussions in blended learning. Internet and Higher Education, 40, 12-19. https://doi.org/10.1016/j.iheduc.2018.09.002

Link DOI | Link Google Scholar

Jackson, N. (2013). The concept of learning ecologies. In N. Jackson & G. Cooper (Eds.), Lifewide learning, education and personal development e-book (pp. 1-21). https://bit.ly/28Jc8As

Link Google Scholar

Kember, D. (2015). Taking qualitative studies beyond findings of a limited number of categories, with motivational orientation as an example. In V. Donche, S. De-Mayer, D. Gijbels, & H. Van-den-Bergh (Eds.), Methodological challenges in research on student learning (pp. 91-106). Antwerp: Garant Publishers. https://bit.ly/2Z2uyUx

Link Google Scholar

Nelson-Laird, T., Seifert, T., Pascarella, E., Mayhew, M., & Blaich, C. (2014). Deeply affecting first-year students’ thinking: Deep approaches to learning and three dimensions of cognitive development. Journal of Higher Education, 85(3), 402-432. https://doi.org/10.1080/00221546.2014.11777333

Link DOI | Link Google Scholar

Patterson, L., & Holladay, R. (2017). Deep learning ecologies: An invitation to complex teaching and learning. Circle Pines, MN: Human Systems Dynamics Institute. https://amzn.to/33tSPGr

Link Google Scholar

Pintrich, P. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385-407. https://doi.org/10.1007/s10648-004-0006-x

Link DOI | Link Google Scholar

Prosser, M., & Trigwell, K. (2017). Student learning and the experience of teaching. HERDSA Review of Higher Education, 4, 5-27. https://bit.ly/2H07sYo

Link Google Scholar

Quardokus, K., & Henderson, C. (2015). Promoting instructional change: Using social network analysis to understand the informal structure of academic departments. Higher Education, 70(3), 315-335. https://doi.org/10.1007/s10734-014-9831-0

Link DOI | Link Google Scholar

Rienties, B., Héliot, Y., & Jindal-Snape, D. (2013). Understanding social learning relations of international students in a large classroom using social network analysis. Studies in Higher Education, 66(4), 489-504. https://doi.org/10.1007/s10734-013-9617-9

Link DOI | Link Google Scholar

Rodríguez-Hidalgo, R., Zhu, C., Questier, F., & Torrens-Alfonso, A. (2011). Using social network analysis for analysing online threaded discussions. International Journal of Learning, Teaching and Educational Research, 10(3), 128-146. https://doi.org/10.1080/00221546.2014.11777333

Link DOI | Link Google Scholar

Tomás-Miquel, J., Expósito-Langa, M., & Nicolau-Juliá, D. (2016). The influence of relationship networks on academic performance in higher education: A comparative study between students of a creative and a non-creative discipline. Higher Education, 71(3), 307-322. https://doi.org/10.1007/s10734-015-9904-8

Link DOI | Link Google Scholar

Vermunt, J., & Donche, V. (2017). A learning patterns perspective on student learning in higher education: State of the art and moving forward. Educational Psychology Review, 29(2), 269-299. https://doi.org/10.1007/s10648-017-9414-6

Link DOI | Link Google Scholar

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press. https://doi.org/10.1017/CBO9780511815478

Link DOI | Link Google Scholar

Fundref

Esta investigación ha sido financiada por el Consejo Australiano de Investigación (2015-2023).

Crossmark

Ficha técnica

Recibido: 30-05-2019

Revisado: 14-07-2019

Aceptado: 01-08-2019

Preprint: 15-11-2019

Fecha publicación: 01-01-2020

Tiempo de revisión del artículo : 45 días | Media tiempo revisión número 62: 40 días

Tiempo de aceptación del artículo: 63 días | Media tiempo aceptación número 62: 72 días

Tiempo de edición del preprint: 171 días | Media tiempo edición número preprint 62: 176 días

Tiempo de edición del artículo: 216 días | Media tiempo edición número 62: 221 días

Métricas

Métricas de este artículo

Vistas: 10254

Lectura del abstract: 8958

Descargas del PDF: 1296

Métricas completas de Comunicar 62

Vistas: 120058

Lectura del abstract: 103313

Descargas del PDF: 16745

Citado por