Achievements and challenges in learning analytics in SpainThe view of SNOLA

  1. Alejandra Martínez-Monés 1
  2. Yannis Dimitriadis 1
  3. Emiliano Acquila-Natale 2
  4. Ainhoa Álvarez 3
  5. Manuel Caeiro-Rodríguez 4
  6. Ruth Cobos 5
  7. Miguel Ángel Conde-González 6
  8. Francisco José García-Peñalvo 7
  9. Davinia Hernández-Leo 8
  10. Iratxe Menchaca Sierra 9
  11. Pedro J. Muñoz-Merino 10
  12. Salvador Ros 11
  13. Teresa Sancho-Vinuesa 12
  1. 1 Universidad de Valladolid, UVa (España)
  2. 2 Universidad Politécnica de Madrid, UPM (España)
  3. 3 Universidad del País Vasco, UPV/EHU (España)
  4. 4 Universidad de Vigo, UVigo (España)
  5. 5 Universidad Autónoma de Madrid, UAM (España)
  6. 6 Universidad de León, ULeón (España)
  7. 7 Universidad de Salamanca, USal (España)
  8. 8 Universitat Pompeu Fabra, UPF (España)
  9. 9 Universidad de Deusto, UDeusto (España)
  10. 10 Universidad Carlos III de Madrid, UC3M (España)
  11. 11 Universidad Nacional de Educación a Distancia, UNED (España)
  12. 12 Universitat Oberta de Catalunya (España)
RIED: revista iberoamericana de educación a distancia

ISSN: 1138-2783

Year of publication: 2020

Issue Title: Analítica del aprendizaje y educación basada en datos: Un campo en expansión

Volume: 23

Issue: 2

Pages: 187-212

Type: Article

DOI: 10.5944/RIED.23.2.26541 DIALNET GOOGLE SCHOLAR lock_openUVADOC editor

More publications in: RIED: revista iberoamericana de educación a distancia


As in other research fields, the development of learning analytics is influenced by the networks of researchers that contribute to it. This paper describes one of such networks: the Spanish Network of Learning Analytics (SNOLA). The paper presents the research lines of the members of SNOLA, as well as the main challenges that learning analytics has to address in the next few years as perceived by these researchers. This analysis is based on SNOLA’s archival data and on a survey carried out to the current members of the network. Although this approach does not cover all the activity related to learning analytics in Spain, the results provide a representative overview of the current state of research related to learning analytics in this context. The paper describes these trends and the main challenges, among which we can point out the need to adopt an ethical commitment with data, to develop systems that respond to the requirements of the end users, and to reach a wider institutional impact

Funding information

This research has been co-funded by the National Research Agency of the Spanish Ministry of Science, Innovation and Universities and the Structural Funds (FSE and FEDER) under project grants RED2018-102725-T, TIN2017-85179-C3-1-R, TIN2017-85179-C3-2-R, TIN2017-85179-C3-3-R and TIN2016-80172-R; by FEDER/Castille and Leon Regional Government grant VA257P18; by the Basque Government under grant number IT980-16 and by the Catalan Government under grant number 2017SGR1619. This work has been co-funded by the Madrid Regional Government, through the project e-Madrid-CM (S2018/TCS-4307), the e-Madrid-CM project is also co-financed by the Structural Funds (FSE and FEDER). D. Hernández-Leo acknowledges the support by ICREA under the ICREA Academia programme.


Bibliographic References

  • 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.
  • 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:
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • Villamañe, M., Alvarez, A., & Larrañaga, M. (2020). CASA, An Architecture to Support Complex Assessment Scenarios. IEEE Access.
  • 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.
  • 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.