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

Applied Sciences

Young Park¹  JuanA. Gómez-Pulido²  Ricardo Soto³ 

[1] Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA;Department of Technologies of Computers and Communications, University of Extremadura, 10003 Cáceres, Spain;School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, 4059 Valparaiso, Chile;

关键词: teaching-enhanced learning and teaching;    personalized learning;    intelligent tutoring systems;    data mining and big data analysis;    intelligent systems;    machine and deep learning;   

DOI  :  10.3390/app10186178

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown