Radical Solutions and Learning Analytics: Personalised Learning and Teaching Through Big Data

by Burgos, Daniel

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Title Statements

Full title :Radical Solutions and Learning Analytics : Personalised Learning and Teaching Through Big Data / edited by Daniel Burgos.

Summary

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 users 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.

Edition Statements

Publication Date:2020 ;
Edition:1st ed. 2020

Publication Statements

Publisher(s):Imprint: Springer, Springer Singapore
Place of Publication:Singapore
Language:eng
Publication Date:2020 ;

The Physical Object

Description:1 online resource (XIV, 227 p. 63 illus., 41 illus. in color.) : online resource.

Edition Identifiers

ISBN:9789811545269, 981154526X
oclc:on1159239028
LC Classification:LC8-6691
Dewey Decimal:371.33

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.

Subjects

Apprentissage Education, Higher Educational Technology Enseignement supérieur Higher Education higher education Instruction Learning Learning & Instruction Technologie éducative Technology and Digital Education