Chapter Introduction
Proposal review
What is learning engineering?
dc.contributor.author | Goodell, Jim | |
dc.date.accessioned | 2023-12-19T13:08:55Z | |
dc.date.available | 2023-12-19T13:08:55Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/86251 | |
dc.description.abstract | This introduction to the book provides a conceptual and historical overview of learning engineering. Although its formal definition is still evolving, learning engineering aims to optimize specific learning solutions--from the learning sciences to human-centered design methodologies to data-informed decision-making--in order to understand under what conditions and with what learners a current design is optimal or not, and to develop and test alternative more robust, or more refined, solutions that are more scalable. The author makes the case for learning engineering as a multidisciplinary approach that complements related professional practices and fields of study such as instructional design, learning sciences, data analytics, instructional systems design, and more. After a brief exploration of the differences between science from engineering, this introduction goes on to address the theoretical and professional origins of learning engineering as well as its inherently team-based process, using as examples the language-learning platform Duolingo and the Carnegie Mellon University spinoff Carnegie Learning, Inc. to discuss effective techniques. | en_US |
dc.language | English | en_US |
dc.subject.other | instructional design; user experience design; data analysis; ISLS; International Society of the Learning Sciences; technology-enhanced learning; artificial intelligence; participatory research design; learning design; engineering design; Janet Kolodner; human-centered learning; Association for the Advancement of Computing in Education; IEDMS; online learning; reskilling; E-Learning; learning sciences; Evidence-Based Practices; computer science; learning analytics; big data; Society for Learning Analytics Research; International Educational Data Mining Society; AECT; Association for Educational Communications and Technologies; ICICLE; design-based research; Learning Engineering Toolkit; lean-agile development; SoLAR; IEEE IC Industry Consortium on Learning Engineering; digital learning; course design; upskilling; massive open online courses; human computer interaction; HCI; Jim Goodell; data science; educational data mining; AACE; educational technologies; MOOC | en_US |
dc.title | Chapter Introduction | en_US |
dc.title.alternative | What is learning engineering? | en_US |
dc.type | chapter | |
oapen.identifier.doi | 10.4324/9781003276579-3 | en_US |
oapen.relation.isPublishedBy | 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb | en_US |
oapen.relation.isPartOfBook | 23f817e1-b552-46eb-8005-3b73c15e8aa4 | en_US |
oapen.relation.isbn | 9781032208503 | en_US |
oapen.relation.isbn | 9781032232829 | en_US |
oapen.imprint | Routledge | en_US |
oapen.pages | 22 | en_US |
peerreview.anonymity | Single-anonymised | |
peerreview.id | bc80075c-96cc-4740-a9f3-a234bc2598f1 | |
peerreview.open.review | No | |
peerreview.publish.responsibility | Publisher | |
peerreview.review.stage | Pre-publication | |
peerreview.review.type | Proposal | |
peerreview.reviewer.type | Internal editor | |
peerreview.reviewer.type | External peer reviewer | |
peerreview.title | Proposal review | |
oapen.review.comments | Taylor & 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). |