Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts

Dear Colleagues,

Analysing and predicting individuals' behaviour are important topics in academic environments, especially after the increasing development and deployment of software tools for supporting learning stages. The automation of many processes involved in the usual students' activity allows for processing massive volumes of data collected from teaching-enhanced learning (TEL) platforms, leading to useful applications for academic personnel. In this way, monitoring and analysing students' behaviour are key activities required for the improvement of students' learning. Recommendations of activities, dropout prediction, performance and knowledge analysis, and resources optimization, among other students-centred interests, are complex tasks that involve many elements that need to be considered. Therefore, it becomes necessary that these efforts search for support from other fields in the 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 intelligent systems, 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 the analysis and prediction of the students' behaviour in academic contexts.

This Special Issue provides a collection of papers of original advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, 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
Guest Editors

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