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000932701 019__ $$a1155873912$$a1156725284$$a1157258119$$a1157727016$$a1158360712
000932701 020__ $$a9789811545269$$q(electronic book)
000932701 020__ $$a981154526X$$q(electronic book)
000932701 020__ $$z9789811545252
000932701 0247_ $$a10.1007/978-981-15-4
000932701 0247_ $$a10.1007/978-981-15-4526-9$$2doi
000932701 035__ $$aSP(OCoLC)on1154312397
000932701 035__ $$aSP(OCoLC)1154312397$$z(OCoLC)1155873912$$z(OCoLC)1156725284$$z(OCoLC)1157258119$$z(OCoLC)1157727016$$z(OCoLC)1158360712
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000932701 049__ $$aISEA
000932701 050_4 $$aLB2822.75
000932701 08204 $$a371.26$$223
000932701 24500 $$aRadical solutions and learning analytics :$$bpersonalised learning and teaching through big data /$$cDaniel Burgos, editor.
000932701 264_1 $$aSingapore :$$bSpringer,$$c[2020]
000932701 264_4 $$c©2020
000932701 300__ $$a1 online resource :$$billustrations.
000932701 336__ $$atext$$btxt$$2rdacontent
000932701 337__ $$acomputer$$bc$$2rdamedia
000932701 338__ $$aonline resource$$bcr$$2rdacarrier
000932701 347__ $$atext file$$bPDF$$2rda
000932701 4901_ $$aLecture notes in educational technology
000932701 504__ $$aIncludes bibliographical references.
000932701 5050_ $$a1 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.
000932701 506__ $$aAccess limited to authorized users.
000932701 520__ $$aLearning 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.
000932701 588__ $$aOnline resource ; title from PDF title page (viewed May 15, 2020).
000932701 650_0 $$aEducation$$xEvaluation.
000932701 650_0 $$aBig data.
000932701 7001_ $$aBurgos, Daniel,$$eeditor.
000932701 77608 $$iPrint version:$$z9789811545252
000932701 830_0 $$aLecture notes in educational technology.
000932701 852__ $$bebk
000932701 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-4526-9$$zOnline Access$$91397441.1
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000932701 980__ $$aEBOOK
000932701 980__ $$aBIB
000932701 982__ $$aEbook
000932701 983__ $$aOnline
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