Educational data mining (EDM) and learning analytics (LA) are two interdisciplinary communities of computer scientists, learning scientists, psychometricians, and researchers from other areas with the same objective of improving learning starting from data. They have grown quickly in the last two decades, and today there is a current increasing interest in moving from the lab to the general market so that EDM/LA results and models can be used by educational institutions and schools worldwide in real-life educational contexts. So, it is necessary to address some important issues about the sustainable application of EDM/LA results/models, such as transferability, scalability, generalizability, applicability, etc. In this way, we can close the EDM/LA loop by using the obtained results and models during the EDM/LA process. The final objective is to obtain useful feedback and recommendations from the different stakeholders (students, instructors, administrators, institutions, etc.) in order to conduct interventions in real educational contexts.

This Special Issue invites researchers interested in the sustainable application of educational data mining and learning analytics. Authors are invited to submit their works addressing one or more of the following topics:

• Transferability for storing knowledge gained while solving one problem and applying it to a different but related problem.
• Effectiveness and efficiency for producing the intended or expected result in the best possible manner with the least waste of time and effort.
• Interpretability and comprehensibility for helping us to understand how the model has learned to solve a given problem and explain the phenomena being modeled.
• Applicability for measuring how a solution is applicable to a problem.
• Generalizability or robustness of a model for measuring its successful application to data sets other than the one used for training and testing.
• Scalability for developing algorithms that are efficient when using very large real-world data sets.

Prof. Dr. Cristóbal Romero Morales
Dr. José María Luna
Guest Editors

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• educational data mining
• learning analytics
• educational data science
• sustainability