The generalization of the use of advanced technological tools in the field of educational is leading to the generation of big data related to academic activities which involve students and teachers. For example, the inclusion of virtual campuses as a regular educational management tool encourages the virtualization of teaching, the online management of grades, the monitoring of student progress, the recording of all kinds of educational variables, etc. In this way, technology-enhanced learning (TEL) platforms allow one to generate and store data that stand out, not only for their huge amount and heterogeneity, but above all, for their link to a time dimension that allows one to analyze and predict student behaviour in its dynamic context, among other purposes.
There are many interesting research lines that deserve to be explored in the education area, such as analyzing and predicting students\' behaviour, developing advanced tools for supporting learning stages, recommending activities, predicting dropout, optimizing resources, etc. For these purposes, there are advanced methods from computational science that have demonstrated a high effectiveness when handling data and processes that are strongly interconnected. Data mining, big data, machine learning, deep learning, collaborative filtering, and recommender systems, among other fields related to artificial intelligence, allow for the development of advanced techniques that provide a significant potential for the above purposes, leading to new applications and more effective approaches in academic analysis and prediction.
This Special Issue provides a collection of papers of original advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, data science, data analytics, big data, and machine learning, especially in the TEL context. Papers about these topics are welcomed.
Prof. Dr. Juan A. Gómez-Pulido
Prof. Dr. Young Park
Prof. Dr. Ricardo Soto
Prof. Dr. José M. Lanza-Gutiérrez