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dc.contributor.authorGoodell, Jim
dc.contributor.authorKolodner, Janet
dc.contributor.authorKessler, Aaron
dc.date.accessioned2024-11-11T10:10:51Z
dc.date.available2024-11-11T10:10:51Z
dc.date.issued2023
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/94544
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.en_US
dc.languageEnglishen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Educationen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JNM Higher education, tertiary educationen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JNV Educational equipment and technology, computer-aided learning (CAL)en_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JNQ Open learning, distance educationen_US
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 Factorsen_US
dc.titleChapter 2 Learning Engineering Applies the Learning Sciencesen_US
dc.typechapter
oapen.identifier.doi10.4324/9781003276579-6en_US
oapen.relation.isPublishedBy7b3c7b10-5b1e-40b3-860e-c6dd5197f0bben_US
oapen.relation.isPartOfBook23f817e1-b552-46eb-8005-3b73c15e8aa4en_US
oapen.relation.isbn9781032208503en_US
oapen.relation.isbn9781032232829en_US
oapen.imprintRoutledgeen_US
oapen.pages37en_US
peerreview.anonymitySingle-anonymised
peerreview.idbc80075c-96cc-4740-a9f3-a234bc2598f1
peerreview.open.reviewNo
peerreview.publish.responsibilityPublisher
peerreview.review.stagePre-publication
peerreview.review.typeProposal
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleProposal review
oapen.review.commentsTaylor & Francis open access titles are reviewed as a minimum at proposal stage by at least two external peer reviewers and an internal editor (additional reviews may be sought and additional content reviewed as required).


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