En el curso virtual aparecerá contenido creado por el equipo docente que completará la biblografía básica.

Además, la bibliografía básica se complementa con los siguientes artículos, que ya aparecen en la sección de los contenidos de esta guía, o secciones de los mismos:

• Joksimovi, S., Kovanovi, V., & Dawson, S. (2019). The journey of learning analytics. HERDSA Review of Higher Education, 6, 27-63.
• Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In Information visualization (pp. 154-175). Springer, Berlin, Heidelberg.
• Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119-135.
• Saqr, M., & Alamro, A. (2019). The role of social network analysis as a learning analytics tool in online problem based learning. BMC medical education, 19(1), 1-11.
• Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
• Romero, C., Romero, J. R., & Ventura, S. (2014). A survey on pre-processing educational data. In Educational data mining (pp. 29-64). Springer, Cham.
• Manjarres, A. V., Sandoval, L. G. M., & Suárez, M. S. (2018). Data mining techniques applied in educational environments: Literature review. Digital Education Review, (33), 235-266.
• Boticario J.G., Aspectos básicos del modelado de usuario en sistemas adaptativos de educación (2022). UNED
• Bull S., Kay J. (2010) Open Learner Models. In: Nkambou R., Bourdeau J., Mizoguchi R. (eds) Advances in Intelligent Tutoring Systems. Studies in Computational Intelligence, vol 308. Springer, Berlin, Heidelberg. DOI:10.1007/978-3-642-14363-2_15 
• B Cook R., Kay J., Kummerfeld B. (2015) MOOClm: User Modelling for MOOCs. In: Ricci F., Bontcheva K., Conlan O., Lawless S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science, vol 9146. Springer, Cham. DOI:10.1007/978-3-319-20267-9_7 
• Bull S., Kay J. (2013) Open Learner Models as Drivers for Metacognitive Processes. In: Azevedo R., Aleven V. (eds) International Handbook of Metacognition and Learning Technologies. Springer International Handbooks of Education, vol 28. Springer, New York, NY. DOI:10.1007/978-1-4419-5546-3_23 
• Kay, J. (2008). Lifelong Learner Modeling for Lifelong Personalized Pervasive Learning. IEEE Transactions on Learning Technologies, 1(4), 215–228. https://doi.org/10.1109/TLT.2009.9
• Paramythis, A., Weibelzahl, S., & Masthoff, J. (2010). Layered evaluation of interactive adaptive systems: Framework and formative methods. User Modeling and User-Adapted Interaction, 20(5), 383–453. https://doi.org/10.1007/s11257-010-9082-4 
• Tran, T. N. T., Felfernig, A., & Tintarev, N. (2021). Humanized recommender systems: State-of-the-art and research issues. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(2), 1-41. 
• Fadel, C., Holmes, W., & Bialik, M. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. The Center for Curriculum Redesign, Boston, MA. Retrieved from https://circls.org/primers/artificial-intelligence-in-education-promises-and-implications-for-teaching-and-learning 
• Calvet Liñán, L., & Juan Pérez, Á. A. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education, 12(3), 98-112.