Logros y retos en analítica del aprendizaje en España: La perspectiva de SNOLA

Alejandra Martínez Monés, Yannis Dimitriadis Damoulis, Emiliano Acquila-Natale, Ainhoa Álvarez, Manuel Caeiro Rodríguez, Ruth Cobos Pérez, Miguel Ángel Conde González, Francisco José García Peñalvo, Davinia Hernández Leo, Iratxe Menchaca Sierra, Pedro José Muñoz-Merino, Salvador Ros, Teresa Sancho Vinuesa

Resumen


Tal y como ocurre en otros campos de investigación, el desarrollo de la analítica del aprendizaje está influido por las redes de investigadores que contribuyen al mismo. Este artículo describe una de estas redes: la Red Española de Analítica de Aprendizaje (SNOLA). El artículo presenta las líneas de investigación de los miembros de SNOLA, así como los principales retos que la analítica del aprendizaje tiene que afrontar en los próximos años desde la visión de estos investigadores. Este análisis está basado en datos de archivo de SNOLA y en una encuesta realizada a los actuales miembros de la red. Aunque esta aproximación no cubre toda la actividad relacionada con analítica del aprendizaje en España, los resultados proporcionan una visión general representativa del estado de la investigación relacionada con analítica del aprendizaje en dicho contexto. El artículo muestra cuáles son estas tendencias y los principales retos, entre los que se encuentran la necesidad de adoptar un compromiso ético con los datos, desarrollar sistemas que respondan a las necesidades de los usuarios y alcanzar mayor impacto institucional.


Palabras clave


grupos y organizaciones, procesamiento de datos, tendencia.

Referencias


Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542–550. https://doi.org/https://doi.org/10.1016/j.chb.2013.05.031

Alexandron, G., Ruipérez-Valiente, J. A., Chen, Z., Muñoz-Merino, P. J., & Pritchard, D. E. (2017). Copying@ Scale: Using harvesting accounts for collecting correct answers in a MOOC. Computers & Education, 108, 96–114.

Amarasinghe, I., Hernández-Leo, D., & Jonsson, A. (2019). Data-informed design parameters for adaptive collaborative scripting in across-spaces learning situations. User Modeling and User-Adapted Interaction, 1–24.

Bote-Lorenzo, M. L., & Gómez-Sánchez, E. (2018). An Approach to Build in situ Models for the Prediction of the Decrease of Academic Engagement Indicators in Massive Open Online Courses. J. UCS, 24(8), 1052–1071.

Caeiro-Rodríguez, M., Hernández-García, Á., & Muñoz-Merino, P. J. (2019). LASI-SPAIN 2019 - Conference Proceedings. Available at: http://ceur-ws.org/Vol-2415/

Chaparro-Peláez, J., Iglesias-Pradas, S., Rodríguez-Sedano, F. J., & Acquila-Natale, E. (2019). Extraction, Processing and Visualization of Peer Assessment Data in Moodle. Applied Sciences, 10(1). https://doi.org/10.3390/app10010163

Claros, I., Cobos, R., & Collazos, C. A. (2015). An Approach Based on Social Network Analysis Applied to a Collaborative Learning Experience. IEEE Transactions on Learning Technologies, 9(2), 190–195. https://doi.org/10.1109/TLT.2015.2453979

Cobos, R, Gil, S., Lareo, Á., & Vargas, F. (2016). Open-DLAs: An open dashboard for learning analytics. In L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale. https://doi.org/10.1145/2876034.2893430

Cobos, R, Jurado, F., & Blázquez-Herranz, A. (2019). A Content Analysis System that supports Sentiment Analysis for Subjectivity and Polarity detection in Online Courses. IEEE Revista Iberoamericana de Tecnologías Del Aprendizaje, 14(4), 177–187. https://doi.org/10.1109/rita.2019.2952298

Cobos, Ruth, & Olmos, L. (2018). A Learning Analytics Tool for Predictive Modeling of Dropout and Certificate Acquisition on MOOCs for Professional Learning. In IEEE International Conference on Industrial Engineering and Engineering Management (Vol. 2018-Decem, pp. 1533–1537). IEEE. https://doi.org/10.1109/IEEM.2018.8607541

Conde, M. A., Colomo-Palacios, R., García-Peñalvo, F. J., & Larrucea, X. (2018). Teamwork assessment in the educational web of data: A learning analytics approach towards ISO 10018. Telematics and Informatics, 35(3), 551–563. https://doi.org/https://doi.org/10.1016/j.tele.2017.02.001

Conde-González, M.Á., & Hernández-García, Á. (2013). A Promised Land for Educational Decision-making?: Present and Future of Learning Analytics. In ACM International Conference Proceeding Series (pp. 239–243). https://doi.org/10.1145/2536536.2536573

Conde-González, M. Á., & Hernández-García, Á. (2019). Learning Analytics: The End of the Beginning. In Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 248–252). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3362789.3362943

de Arriba-Pérez, F., Caeiro-Rodríguez, M., & Santos-Gago, J. M. (2018). How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators. Journal of Ambient Intelligence and Humanized Computing, 9(4), 897–917. https://doi.org/10.1007/s12652-017-0477-5

Drachsler, H. & Greller, W. (2016). Privacy and Analytics – it’s a DELICATE issue. A Checklist to establish trusted Learning Analytics. 6th Learning Analytics and Knowledge Conference 2016, April 25-29, 2016, Edinburgh, UK.

EC (2016). Learning Analytics: Key messages. Retrieved from https://ec.europa.eu/education/sites/education/files/2016-pla-learning-analytics_en.pdf

EU (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC. Off. J. Eur. Union, L119 (2016), pp. 1-88.

Er, E., Dimitriadis, Y., & Gaseviç, D. (2019). An analytics-driven model of dialogic peer feedback. In 13th International Conference on Computer Supported Collaborative Learning (CSCL 2019),. Lyon, France.

Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., … Vuorikari, R. (2016). Research evidence on the use of learning analytics: Implications for education policy.

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.

Gómez-Aguilar, D. A., García-Peñalvo, F. J., & Therón, R. (2014). Analítica visual en e-learning. Profesional de La Información, 23(3), 236–245. https://doi.org/10.3145/epi.2014.may.03

Guerrero-Higueras, Á. M., DeCastro-García, N., Rodriguez-Lera, F. J., Matellán, V., & Conde, M. Á. (2019). Predicting academic success through students’ interaction with Version Control Systems. Open Computer Science, 9(1), 243–251.

Hernández-García, Á., Acquila-Natale, E., Chaparro-Peláez, J., & Conde, M. Á. (2018). Predicting teamwork group assessment using log data-based learning analytics. Computers in Human Behavior, 89, 373–384. https://doi.org/https://doi.org/10.1016/j.chb.2018.07.016

Hernández-García, Á., & Suárez-Navas, I. (2017). GraphFES: A Web Service and Application for Moodle Message Board Social Graph Extraction. In B. Kei Daniel (Ed.), Big Data and Learning Analytics in Higher Education: Current Theory and Practice (pp. 167–194). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-06520-5_11

Latour, B. (2005). Reassembling the social. An introduction to actor-network-theory. Oxford: Oxford University Press.

Omedes, J. (2018). Learning Analytics 2018 - An updated perspective. Retrieved January 20, 2020, from https://www.iadlearning.com/learning-analytics-2018.

Manso-Vázquez, M., Caeiro-Rodríguez, M., & Llamas-Nistal, M. (2018). An xAPI Application Profile to Monitor Self-Regulated Learning Strategies. IEEE Access, 6, 42467–42481.

Martínez, J. A., Campuzano, J., Sancho-Vinuesa, T., & Valderrama, E. (2019). Predicting student performance over time. A case study for a blended-learning engineering course. CEUR Workshop Proceedings, 2415, 43–55. Retrieved from http://ceur-ws.org/Vol-2415/paper05.pdf

Menchaca Sierra, I., Guenaga, M., & Solabarrieta, J. (2018). Learning analytics for formative assessment in engineering education. The International Journal of Engineering Education, 34(3), 953–967.

Michos, K., Hernández-Leo, D., & Albó, L. (2018). Teacher-led inquiry in technology-supported school communities. British Journal of Educational Technology, 49(6), 1077–1095.

Moreno-Marcos, P. M., Muñoz-Merino, P. J., Maldonado-Mahauad, J., Pérez-Sanagustín, M., Alario-Hoyos, C., & Kloos, C. D. (2020). Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Computers & Education, 145, 103728.

Muñoz-Merino, P. J., Novillo, R. G., & Kloos, C. D. (2018). Assessment of skills and adaptive learning for parametric exercises combining knowledge spaces and item response theory. Applied Soft Computing, 68, 110–124.

Peña-Ayala, A. (2018). Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy. WIREs Data Mining and Knowledge Discovery, 8(3), e1243. https://doi.org/10.1002/widm.1243

Rodríguez-Triana, M. J., Martínez-Monés, A., Asensio-Pérez, J. I., & Dimitriadis, Y. (2015). Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. British Journal of Educational Technology, 46(2), 330–343.

Ros, S., Lázaro, J. C., Robles-Gómez, A., Caminero, A. C., Tobarra, L., & Pastor, R. (2017). Analyzing Content Structure and Moodle Milestone to Classify Student Learning Behavior in a Basic Desktop Tools Course. In Proc. of Intl. Conference Technological Ecosystems for Enhancing Multiculturality (TEEM). Cádiz, Spain.

Rubio-Fernández, A., Muñoz-Merino, P. J., & Delgado Kloos, C. (2019). A learning analytics tool for the support of the flipped classroom. Computer Applications in Engineering Education, 27(5), 1168–1185.

Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Leony, D., & Kloos, C. D. (2015). ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. Computers in Human Behavior, 47, 139–148.

Ruiz, S., Urretavizcaya, M., Rodríguez, C., & Fernández-Castro, I. (2018). Predicting students’ outcomes from emotional response in the classroom and attendance. Interactive Learning Environments, 1–23. https://doi.org/10.1080/10494820.2018.1528282

Slade, S., & Prinsloo, P. (2013). Learning analytics ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529

Tobarra, L., Ros, S., Hernández, R., Robles-Gómez, A., Caminero, A. C., & Pastor, R. (2014). Integrated Analytic dashboard for virtual evaluation laboratories and collaborative forums. In 2014 XI Tecnologias Aplicadas a la Ensenanza de la Electronica (Technologies Applied to Electronics Teaching)(TAEE) (pp. 1–6).

Tobarra, L., Ros, S., Hernández, R., Robles-Gómez, A., Pastor, R., Caminero, A. C., … Claramonte, J. (2017). Analyzing Students’ Behavior in UNED-COMA MOOCs. In LASI-SPAIN (pp. 124–137).

Tsai, Y.-S., Gašević, D., Whitelock-Wainwright, A., Muñoz-Merino, P. J., Moreno-Marcos, P. M., Fernández, A. R., … others. (2018). SHEILA: Support Higher Education to Integrate Learning Analytics.

Vázquez-Ingelmo, A., García-Peñalvo, F. J., & Therón, R. (2019). Taking advantage of the software product line paradigm to generate customized user interfaces for decision-making processes: A case study on university employability. PeerJ Computer Science, 5. https://doi.org/10.7717/peerj-cs.203

Villamañe, M., Alvarez, A., & Larrañaga, M. (2020). CASA, An Architecture to Support Complex Assessment Scenarios. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2966595

Villamañe, M., Larrañaga, M., & Álvarez, A. (2017). Rating monitoring as a means to mitigate rater effects and controversial evaluations. In Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality- TEEM 2017 (pp. 1–8). Cádiz, Spain: ACM Press. https://doi.org/10.1145/3144826.3145389

Vujovic, M., & Hernández-Leo, D. (2019). Shall We Learn Together in Loud Spaces? Towards Understanding the Effects of Sound in Collaborative Learning, 891–892.

Wiley, K., Dimitriadis, Y., Bradford, M., & Linn, M. (2020). From Theory to Action: Developing and Evaluating Learning Analytics for Learning Design. In Learning Analytics and Knowledge Conference (LAK 2020). Frankfurt, Germany.




DOI: https://doi.org/10.5944/ried.23.2.26541

Enlaces refback

  • No hay ningún enlace refback.




RIED. Revista Iboeroamericana de Educación a Distancia
(La Revista Iberoamericana de la Educación Digital)
Director/Editor : Lorenzo García Aretio
UNED, Facultad de Educación
C/ Juan del Rosal, 14
28040 Madrid (Spain).
ried@edu.uned.es
ISSN :1138-2783
E-ISSN : 1390-3306
Depósito Legal : M- 36.279 -1997
Edita: Asociación Iberoamericana de Educación Superior a Distancia (AIESAD).
 Madrid (España).

Reconocimiento NoComercial (by-nc): Se permite la generación de obras derivadas siempre que no se haga un uso comercial. Tampoco se puede utilizar la obra original con finalidades comerciales.
SÍGUENOS EN:

https://2.bp.blogspot.com/-wtzwURZeg6I/V_y8vM5DmdI/AAAAAAABKKQ/y_fW6U2dW3cOLG6z-tUwJ9u1Pwt9ltXHACLcB/s320/blogger_b_logo.jpg https://4.bp.blogspot.com/-Q3lAzaCezXA/V_TZ0BTuIkI/AAAAAAABKF4/wP8QRQVCPiQnk0sE7nEDnZHY5F03AOjbgCLcB/s200/twitrer_120%2B%25281%2529.jpg https://4.bp.blogspot.com/-4So1RLxqN7Q/VHMWABdXX9I/AAAAAAAAb4E/mV00Ac5Gm-Q/s1600/fb_icon_325x325.png https://1.bp.blogspot.com/-S7ecZmnt3os/Vzmf77J7EfI/AAAAAAABEYc/g3MJ_0z_noUtAiLS7MRRHXgzOkGbZbfUACLcB/s200/scholar_logo_lg_2011.gif

Colaboran con RIED:

https://2.bp.blogspot.com/-VKcDNIR3Sqk/V_aPanb6P0I/AAAAAAABKIA/XSdUeendX2wJ_afKOCIIkxkZjW0ZnT0vACLcB/s320/logoCUED.jpg       https://3.bp.blogspot.com/-wxw5W-VCRGA/WAnp69yeyuI/AAAAAAABKgo/LHi490KturcyZQE7KnlK2ZT9taWEUXkgQCLcB/s320/logo-AM2.01.png    Alteridad