Afficher la notice abrégée

dc.contributor.authorGoodell, Jim
dc.contributor.authorKolodner, Janet
dc.contributor.authorKessler, Aaron
dc.date.accessioned2025-03-08T00:54:08Z
dc.date.available2025-03-08T00:54:08Z
dc.date.issued2023
dc.date.submitted2024-11-11T10:10:51Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/94544
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/173450
dc.description.abstractThe Learning Engineering Toolkit is a practical guide to the rich and varied applications of learning engineering, a rigorous and fast-emerging discipline that synthesizes the learning sciences, instructional design, engineering design, and other methodologies to support learners. As learning engineering becomes an increasingly formalized discipline and practice, new insights and tools are needed to help education, training, design, and data analytics professionals iteratively develop, test, and improve complex systems for engaging and effective learning. Written in a colloquial style and full of collaborative, actionable strategies, this book explores the essential foundations, approaches, and real-world challenges inherent to ensuring participatory, data-driven, learning experiences across populations and contexts.
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JNM Higher education, tertiary education
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JNV Educational equipment and technology, computer-aided learning (CAL)
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JNQ Open learning, distance education
dc.subject.otherLearning Science Discoveries,Competency Definitions,Playing Store,Learning Engineering,Mental Models,Human Computer Interaction Institute,Learning Sciences,Learning Sciences Framework,Assessment Events,Formative Performance Assessments,Desirable Difficulty,Chess Masters,Cognitive Load,Long Term Memory,Learner Variability,Negative Numbers,Fractional Quantities,Imperfect Understanding,Background Knowledge,Wo,Light Switch,Beatles,Noncognitive Factors
dc.titleChapter 2 Learning Engineering Applies the Learning Sciences
dc.typechapter
oapen.identifier.doi10.4324/9781003276579-6
oapen.relation.isPublishedByfa69b019-f4ee-4979-8d42-c6b6c476b5f0
oapen.relation.isPartOfBookLearning Engineering Toolkit
oapen.relation.isbn9781032208503
oapen.relation.isbn9781032232829
oapen.imprintRoutledge
oapen.pages37
peerreview.review.typeProposal
peerreview.anonymitySingle-anonymised
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityPublisher
peerreview.idbc80075c-96cc-4740-a9f3-a234bc2598f1
peerreview.titleProposal review


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée

open access
Excepté là où spécifié autrement, la license de ce document est décrite en tant que open access