Radical Solutions and Learning Analytics

Personalised Learning and Teaching Through Big Data
1. Auflage 2020
ISBN: 978-981-15-4525-2
Verlag: Springer Nature Singapore

Buch, Englisch, 227 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 535 g

Reihe: Lecture Notes in Educational Technology

Learning Analytics become the key for Personalised Learning and Teaching thanks to the storage, categorisation and smart retrieval of Big Data. Thousands of user data can be tracked online via Learning Management Systems, instant messaging channels, social networks and other ways of communication. Always with the explicit authorisation from the end user, being a student, a teacher, a manager or a persona in a different role, an instructional designer can design a way to produce a practical dashboard that helps him improve that very user’s performance, interaction, motivation or just grading. This book provides a thorough approach on how education, as such, from teaching to learning through management, is improved by a smart analysis of available data, making visible and useful behaviours, predictions and patterns that are hinder to the regular eye without the process of massive data.

Zielgruppe

Research

Autoren/Hrsg.

Burgos, Daniel

Fachgebiete

• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik Methoden des Lehrens und Lernens
• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik E-Learning, Bildungstechnologie
• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik Allgemeine Didaktik Hochschuldidaktik

Inhaltsverzeichnis

1 Learning Analytics as a Breakthrough in Educational Improvement.- 2 LA to Improve the Learner's Performance.- 3 LA to Improve the Teacher's Performance.- 4 Dashboards for a Better Application of LA.- 5 Mobile LA in Digital Devices.- 6 Physical Sensors and LA in the Classroom.- 7 Remote Labs and Big Data.- 8 Understanding Big Data for Educational Management.- 9 Interpretation of Live Data and Decision Making in Streamed Lessons and Real-Time User Tracking.- 10 Prediction of Users' Behaviour.- 11 Prevention of Students and Faculty Attrition.- 12 Personalised Mentoring Through Quantitative & Qualitative Data.- 13 User Vectorisation Through Deep Learning and Neural Networks.- 14 Fighting Student's Drop-Out Through Historical Data.- 15 Visual Analytics for a Better Impact of Deep Data.

Über Autor(innen)

Prof. Dr. Daniel Burgos works as a Full Professor of Technologies for Education & Communication and Vice-rector for International Research (UNIR Research), at Universidad Internacional de La Rioja (UNIR). In addition, he holds the UNESCO Chair on eLearning and the ICDE Chair in Open Educational Resources. He works also as Director of the Research Institute for Innovation & Technology in Education (UNIR iTED).
His interests are mainly focused on Educational Technology & Innovation: Adaptive/Personalised and Informal eLearning, Learning Analytics, Social Networks, eGames, and eLearning Specifications. He has published over 130 scientific papers, 15 books and 15 special issues on indexed journals. He is or has been involved in +55 European and Worldwide R&D projects, with a practical implementation approach.
He also works as a Professor at An-Najah National University (Palestine), an Assistant Professor at Universidad Nacional de Colombia (UNAL, Colombia), and a Visiting Professor at Coventry University (United Kingdom) and Universidad de las Fuerzas Armadas (ESPE, Ecuador). He has been chair (2016, 2018) and vice-chair (2015, 2017) of the international jury for the UNESCO King Hamad Bin Isa Al Khalifa Prize for the Use of ICTs in Education. He is a consultant for United Nations Economic Commission for Europe (UNECE), European Commission, European Parliament, Russian Academy of Science and ministries of Education in over a dozen countries. He is an IEEE Senior Member. He holds degrees in Communication (PhD), Computer Science (Dr. Ing), Education (PhD), Anthropology (PhD), Business Administration (DBA) and Artificial Intelligence (MIT, postgraduate).