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

doi:10.3390/books978-3-0365-2116-9

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

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October 2021

370 pages

ISBN978-3-0365-2115-2 (Hardback)
ISBN978-3-0365-2116-9 (PDF)

https://doi.org/10.3390/books978-3-0365-2116-9

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This book is a reprint of the Special Issue Advanced Techniques in the Analysis and Prediction of Students' Behaviour in Technology-Enhanced Learning Contexts that was published in

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Chemistry & Materials Science

Computer Science & Mathematics

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Environmental & Earth Sciences

Physical Sciences

Summary

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, resource optimization, etc. However, these improvements would not be possible without the application of computer science techniques that have demonstrated 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.

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Keywords

dropout; machine learning; big data; class-imbalance; oversampling; ensemble; petri nets; simulation; transitions; probability; concurrency; LMS; knowledge-based method; topic-modeling; sentiment analysis; machine learning; complex networks; Computational Fluid Dynamic (CFD); fluid-mechanics; teaching-learning; engineering education; computer applications; classification problem; machine learning; at-risk students; machine learning; learning management system; blended learning; introduction to programming; automatic machine learning; educational data mining; Bayesian optimization; early performance prediction; Educational Data Mining; predicting student performance; student model portability; prediction; students’ performance; dropout; machine learning; supervised learning; unsupervised learning; collaborative filtering; recommender systems; artificial neural networks; deep learning; machine learning; GIS; mental stress; multiple regression; face reading; creativity; computer mediated communication; face-to-face; media richness; organizational learning; student performance; kindergarten children; nearest neighbor; imbalanced data; Open Source Software (OSS); Technology Acceptance Model (TAM); Self-Determination Theory (SDT); behavioral intention to use; student; secondary school; classification algorithms; data preprocessing; data mining; data transformation; student academic performance; video learning analytics; at-risk students; genetic algorithm; learning analytics; educational data mining; early warning system; artificial intelligence; predictive models; personalized feedback; online learning; introductory programming courses; dropout prediction; automated assessment; source code evaluation; microlearning; cloud computing; technology adoption; higher education institutions; SEM; neural network; teaching-enhanced learning and teaching; personalized learning; intelligent tutoring systems; data mining and big data analysis; intelligent systems; machine and deep learning; recommender systems; software tools; performance prediction; knowledge analysis