Mapping the situation of educational technologies in the spanish university system using social network analysis and visualization

  1. Benjamín Vargas-Quesada 1
  2. Carmen Zarco 2
  3. Oscar Cordón 1
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

  2. 2 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Volumen: 8

Número: 2

Páginas: 190-201

Tipo: Artículo

DOI: 10.9781/IJIMAI.2021.09.004 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

Educational Technologies (EdTech) are based on the use of Information and Communication Technologies (ICT) to improve the quality of teaching and learning. EdTech is experiencing great development at different educational levels worldwide, especially since the appearance of Covid-19. The recent publication of a study by the ICT Sectorial of CRUE Universidades Españolas, the Spanish University Association, is the first report on the implementation of such technologies within Spain´s University System. This paper presents two different maps based on the data from that report. Together, they illustrate the penetration of different types of EdTech in our university system and shed light on the strategic interest behind their adoption. Our goal is to produce self-explanatory maps that can be easily and directly interpreted. The first map reflects wide granularity in terms of the global importance of technologies, while the second points to relevant conclusions given the spatial position of Spain´s universities, and the size of the nodes that represent them (directly related with their strategic interests on EdTech), as well as with the local relationships existing among them (identifying similarities on those strategic interests).

Referencias bibliográficas

  • T. J. Newby, D. Stepich, J. Lehman, J. D. Russell, and A.T. Leftwich, Educational Technology for Teaching and Learning, 4th ed., New York: Pearson, 2010.
  • T. Gray and H. Silver-Pacuilla, Eds., Breakthrough teaching and learning: how educational and assistive technologies are driving innovation, New York: Springer-Verlag, 2011.
  • B. Alexander, et al., “EDUCAUSE Horizon Report: 2019 Higher Education Edition”, Louisville, CO: EDUCAUSE, 2019.
  • R. Ferguson, et al., “Innovating pedagogy 2017: Open university innovation report #6”, Milton Keynes: The Open University, 2019. Accessed: Feb. 06, 2021. Available: https://iet.open.ac.uk/file/ innovating-pedagogy-2017.pdf.
  • J-M. Lowendahl, T-L. Thayer, and G. Morgan. “Top 10 strategic technologies impacting higher education in 2016”. Accessed: Feb. 06, 2021. Available: https://www.gartner.com/en/documents/3186323/ top-10-strategic-technologies-impacting-higher-education.
  • J. King, and J. South. “Reimagining the Role of Technology in Higher Education. A Supplement to the National Education Technology Plan”, Washington: U.S. Department of Education, Office of Educational Technology, 2019. Accessed: Feb. 06, 2021. Available: https:// tech.ed.gov/files/2017/01/Higher-Ed-NETP.pdf.
  • R. Walker, et al. “Survey of technology enhanced learning for higher education in the UK”, Oxford: Universities and Colleges Information Systems Association, 2016. Accessed: Feb. 06, 2021. Available: https://www.ucisa.ac.uk/bestpractice/surveys/tel/tel.aspx
  • J. Gómez Ortega, et al. “Informe de situación de las Tecnologías Educativas en las universidades españolas 2018” (in Spanish). Madrid: Crue Universidades Españolas, 2019. Accessed: Feb. 06, 2021. Available: https://tic.crue.org/publicaciones/#folte.
  • S. Wasserman, and K. Faust, Social network analysis: methods and applications (structural analysis in the social sciences), Cambridge: University Press, 1994.
  • A. Santos, et al., “Estado de situación de las Tecnologías Aplicadas a la enseñanza y el aprendizaje en la Educación Superior argentina” (in Spanish). Buenos Aires: Metared, 2019, Accessed: Feb. 10, 2021. Available: https://www.metared.org/argentina/wp-content/uploads/ sites/11/2019/10/Estado-de-Situacion-TAEA-Educacion-Superior_ Metared-Argentina.pdf.
  • J. L. Ponce-López, C. M. Vicario-Solórzano, and F. López-Valencia. “Estado Actual de las Tecnologías Educativas en las Instituciones de Educación Superior en México” (in Spanish). ANUIES, México, 2021. Accessed: Feb. 21, 2021. Available: https://estudio-tic.anuies.mx/#estado_te.
  • M. Miller, Ditch That Textbook: Free Your Teaching and Revolutionize Your Classroom, Michigan: Dave Burgess Consulting, 2015.
  • P. A. Rodríguez, V. Tabares, N. D. Duque, D. A. Ovalle, and R. M. Vicari, “BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 2, no. 1, pp. 6-11, 2013, doi:10.9781/ijimai.2013.211.
  • G. Allen. The New Pillars of Modern Teaching, Bloomington: Solution Tree Press, 2016.
  • L. Kolb. Learning First, Technology Second: The Educator‘s Guide to Designing Authentic Lessons, Portland, Oregon: International Society for Technology Education, 2017.
  • L. De-Marcos, E. García-Lopez, A., and García-Cabot. “On the effectiveness of game-like and social approaches in learning: Comparing educational gaming, gamification & social networking”, Computers & Education, vol. 95, pp. 99-113, 2016, doi:10.1016/j.compedu.2015.12.008.
  • J. Nouri. “The flipped classroom: for active, effective and increased learning - especially for low achievers”, International Journal of Educational Technology in Higher Education, vol. 13, no. 33, 2016, doi: 10.1186/s41239-016-0032-z.
  • Á. Martínez Navarro and P. Moreno-Ger. “Comparison of clustering algorithms for learning analytics with educational datasets”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 2, pp. 9-16, 2018, doi: 10.9781/ijimai.2018.02.003.
  • R. Klamma, P. de Lange, A.T. Neumann, and P. Nicolaescu, “An Integrated Learning Analytics Approach for Virtual Vocational Training Centers”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 2, pp. 32-38, 2018, doi:10.9781/ijimai.2018.02.006.
  • J. M. Spector, “Remarks on MOOCS and Mini-MOOCS”, Educational Technology Research and Development, vol. 62, no. 3, pp. 385-392, 2014.
  • S. Margoum, R. Bendaoud, K. Berrada, and A. Idrissi. “UC@MOOC’s Effectiveness by Producing Open Educational Resources”, International Journal of Interactive Multimedia And Artificial Intelligence, vol. 5, no. 2, pp. 58-62, 2018, doi:10.9781/ijimai.2018.02.007.
  • J. Gómez Ortega, et al., “UNIVERSITIC 2017, Análisis de las TIC en las universidades españolas” (in Spanish). Madrid: Crue Universidades Españolas, 2017. Accessed: Feb. 06, 2021. Available: http://tic. crue.org/publicaciones/informe-universitic-2017.
  • M. Koehler, and P. Mishra, “What is technological pedagogical content knowledge (TPACK)?” Contemporary Issues in Technology and Teacher Education, vol. 9, no 1, pp. 60-70, 2009.
  • D. P. Pancho, J. M. Alonso, O. Cordón. A. Quirin and L. Magdalena. “FINGRAMS: visual representations of fuzzy rule-based inference for expert analysis of comprehensibility”, IEEE Transactions on Fuzzy Systems, vol. 21, no 6, pp. 1133-1149. 2013.
  • E. Serrano, A. Quirin, J. Botia, and O. Cordón, “Debugging complex software systems by means of pathfinder networks”, Information Sciences, vol. 180, no 5, pp. 561-583, 2010.
  • Trawinski, M. Chica, D. Pancho, S. Damas, and O. Cordón, “moGrams: a network-based methodology for visualizing the set of non-dominated solutions in multiobjective optimization”, IEEE Transactions on Cybernetics., vol. 48, no 2, pp. 474-485, 2018.
  • C. Zarco, C. E. Santos, and O. Cordón, “Advanced visualization of Twitter data for its analysis as a communication channel in traditional companies”, Progress in Artificial Intelligence, vol. 8, no 3, pp. 327-333, 2019.
  • B. Vargas-Quesada, Z. Chinchilla-Rodríguez, and N. Rodriguez, “Identification and visualization of the intellectual structure in graphene research”, Frontiers. Research. Metrics. Analytics, vol. 2, no 7. 2017. Accessed: Feb. 06, 2021. Available: https://doi.org/10.3389/ frma.2018.00013.
  • A. Quirin, O. Cordón, J. Santamaría, B. Vargas-Quesada, and F. MoyaAnegón, “A new variant of the pathfinder algorithm to generate large visual science maps in cubic time”, Information Processing & Management, vol. 44, no 4, pp. 1611-1623, 2008.
  • X. Lin, H. D. X. White, and J. Buzydlowski, J. “Real-time author cocitation mapping for online searching”, Information Processing & Management, vol 39, no. 5, pp. 689-706. 2003, doi.org/10.1016/S0306- 4573(02)00037-7
  • N. J. van Eck, and L. Waltman, L. “Visualizing bibliometric networks”, In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods and practice, pp. 285–320. Springer, 2018.
  • Author 3, 2008.
  • T. Kamada, and S. Kawai, “An algorithm for drawing general undirected graphs”, Information Processing Letters, vol. 31, no 1, pp. 7-15. 1989.
  • A. Unwin, M. Theus, and H. Hofmann, Graphics of large datasets: visualizing a million, New York: Springer Science & Business Media, 2008.
  • C. Chen, and S., “Visualizing evolving networks: Minimum spanning trees versus pathfinder networks”, in Proceedings of IEEE Symposium on Information Visualization, Seattle, USA, pp. 67–74, 2003, doi:10.1109/ INFVIS.2003.1249010. Regular Issue - 201 -
  • R. Schvaneveldt, F. Durso, and D. Dearholt, “Network structures in proximity data”, Psychology of Learning and Motivation, vol. 24, pp. 249- 284. 1989.
  • S. P. Borgatti, and M. G. Everett, “Models of core/periphery structures”, Social Networks, vol. 21, no 4, pp. 375-395, 1999.kk
  • N. J. Van Eck, and L. Waltman. “Software survey: VOSviewer, a computer program for bibliometric mapping”, Scientometrics, vol. 84, no 2, pp. 523- 538. 2010.
  • S. Fortunato. “Community detection in graphs”. Physics Reports, vol. 486, no 3-5, pp. 75–174, 2010, doi: 10.1016/j.physrep.2009.11.002.
  • V. D. Blondel, J. L. Guillaume, R. Lambiotte, and E. Lefebvre. “Fast unfolding of communities in large networks”. Journal of Statistical Mechanics: Theory and Experiment, vol. 10:10008, 2008, doi:10.1088/1742- 5468/2008/10/P10008.
  • V. A. Traag, L. Waltman, and N. J. van Eck. “From Louvain to Leiden: guaranteeing well-connected communities”. Scientific Reports, vol. 9: 5233, 2019, doi:10.1038/s41598-019-41695-z.
  • A. Clauset, M. E. J. Newman, and C. Moore. “Finding community structure in very large networks”. Physical Review E, vol. 70:066111, 2004, doi: 10.1103/PhysRevE.70.066111.
  • C. Romero, and S. Ventura, “Guest Editorial: Special Issue on Early Prediction and Supporting of Learning Performance”, IEEE Transactions on Learning Technologies, vol. 12, no. 2, pp. 145-147, 2019, doi:10.1109/ TLT.2019.2908106