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001445408 001__ 1445408
001445408 003__ OCoLC
001445408 005__ 20230310003829.0
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001445408 019__ $$a1306024087$$a1306057253$$a1324252021$$a1351302891$$a1351983981
001445408 020__ $$a9783030966447$$q(electronic bk.)
001445408 020__ $$a3030966445$$q(electronic bk.)
001445408 020__ $$z9783030966430
001445408 020__ $$z3030966437
001445408 0247_ $$a10.1007/978-3-030-96644-7$$2doi
001445408 035__ $$aSP(OCoLC)1305913375
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001445408 049__ $$aISEA
001445408 050_4 $$aLB1028.43$$b.D38 2022eb
001445408 08204 $$a371.33/4$$223
001445408 24500 $$aData analytics in e-learning :$$bapproaches and applications /$$cMarian Cristian Mihăescu, editor.
001445408 264_1 $$aCham :$$bSpringer,$$c[2022]
001445408 264_4 $$c©2022
001445408 300__ $$a1 online resource (vii, 165 pages : illustrations (some color)).
001445408 336__ $$atext$$btxt$$2rdacontent
001445408 337__ $$acomputer$$bc$$2rdamedia
001445408 338__ $$aonline resource$$bcr$$2rdacarrier
001445408 347__ $$atext file$$bPDF$$2rda
001445408 4901_ $$aIntelligent systems reference library,$$x1868-4408 ;$$vvolume 220
001445408 504__ $$aReferences.
001445408 504__ $$aIncludes bibliographical references.
001445408 50500 $$tIntroduction to Data Analytics in e-Learning --$$tPublic Datasets and Data Sources for Educational Data Mining --$$tBuilding Data Analysis Workflows that Provide Personalized Recommendations for Students --$$tBuilding Interpretable Machine Learning Models with Decision Trees --$$tEnhancing Machine Learning Models by Augmenting New Functionalities --$$tIncreasing Engagement in e-Learning Systems --$$tUsability Evaluation Roadmap for e-Learning Systems --$$tDeveloping new algorithms that suite specific application requirements.
001445408 506__ $$aAccess limited to authorized users.
001445408 520__ $$aThis book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
001445408 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 1, 2022).
001445408 650_0 $$aArtificial intelligence$$xEducational applications.
001445408 650_0 $$aMachine learning.
001445408 650_6 $$aIntelligence artificielle$$xApplications en éducation.
001445408 650_6 $$aApprentissage automatique.
001445408 655_0 $$aElectronic books.
001445408 7001_ $$aMihăescu, Marian Cristian,$$eeditor.
001445408 77608 $$iPrint version:$$z3030966437$$z9783030966430$$w(OCoLC)1293059996
001445408 830_0 $$aIntelligent systems reference library ;$$vv. 220.$$x1868-4408
001445408 852__ $$bebk
001445408 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-96644-7$$zOnline Access$$91397441.1
001445408 909CO $$ooai:library.usi.edu:1445408$$pGLOBAL_SET
001445408 980__ $$aBIB
001445408 980__ $$aEBOOK
001445408 982__ $$aEbook
001445408 983__ $$aOnline
001445408 994__ $$a92$$bISE