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

Abstract

The development and promotion of teaching-enhanced learning tools in the academic field is leading to the collection of a large amount of data generated from the usual activity of students and teachers. The analysis of these data is an opportunity to improve many aspects of the learning process: recommendations of activities, dropout prediction, performance and knowledge analysis, resources optimization, etc. However, these improvements would not be possible without the application of computer science techniques that have demonstrated a high effectiveness for this purpose: data mining, big data, machine learning, deep learning, collaborative filtering, and recommender systems, among other fields related to intelligent systems. This Special Issue provides 17 papers that show advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, big data, and machine learning in the teaching-enhanced learning context.

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

“…In this scenario, and with the increasing development and implementation of learning support software tools, the analysis and prediction of student behaviour, including dropout, are crucial aspects of teaching environments, particularly in higher education. Here, the monitoring and analysis of student behaviour are fundamental key activities since they can contribute to improve students' learning [4] and decrease the level of school dropout. According to Bañeres et al [5], tools based on automatic recommendations are examples of systems that can improve the way learning processes are currently carried out and enhance the work of teachers in learning environments with a large number of students.…”