LEADER 04108oam 2200673Ma 450 001 9910141011303321 005 20241204160533.0 010 $a9780262294164 010 $a0262294168 024 8 $a9786613020123 035 $a(CKB)2670000000079843 035 $a(EBL)3339209 035 $a(SSID)ssj0000486864 035 $a(PQKBManifestationID)12212894 035 $a(PQKBTitleCode)TC0000486864 035 $a(PQKBWorkID)10449740 035 $a(PQKB)11688114 035 $a(MiAaPQ)EBC3339209 035 $a(OCoLC)708738085$z(OCoLC)741251222$z(OCoLC)961635242$z(OCoLC)962614621$z(OCoLC)968296310$z(OCoLC)988473695$z(OCoLC)1045475327 035 $a(OCoLC-P)708738085 035 $a(MaCbMITP)8909 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/78506 035 $a(ScCtBLL)d96731ce-8415-4a0e-9b16-31e348f0a6fc 035 $a(OCoLC)1139863917 035 $a(oapen)doab78506 035 $a(EXLCZ)992670000000079843 100 $a20100416d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aQuest to learn $edeveloping the school for digital kids /$fKatie Salen [and others] 210 $aCambridge, Mass. $cMIT Press$dİ2011 215 $a1 online resource (164 p.) 225 1 $aThe John D. and Catherine T. MacArthur Foundation reports on digital media and learning 300 $aDescription based upon print version of record. 311 08$a9780262294171 311 08$a0262294176 311 08$a9780262515658 311 08$a0262515652 320 $aIncludes bibliographical references. 327 $a""Contents""; ""Series Forward""; ""Preface""; ""About This Document""; ""Ten Core Practices Defining Quest to Learn""; ""Glossary""; ""Background""; ""Mission""; ""The Quest to Learn Community""; ""Game-Based Learning and Knowing""; ""Curriculum and Instruction""; ""Curriculum Structure""; ""Key Characteristics""; ""Sample Discovery Mission and Quests""; ""School Design Team""; ""References"" 330 $aThe design for Quest to Learn, an innovative school in New York City that offers a "game-like" approach to learning. Quest to Learn, an innovative school for grades 6 to 12 in New York City, grew out of the idea that gaming and game design offer a promising new paradigm for curriculum and learning. The designers of Quest to Learn developed an approach to learning that draws from what games do best: drop kids into inquiry-based, complex problem spaces that are built to help players understand how they are doing, what they need to work on, and where to go next. Content is not treated as dry information but as a living resource; students are encouraged to interact with the larger world in ways that feel relevant, exciting, and empowering. Quest to Learn opened in the fall of 2009 with 76 sixth graders. In their first semester, these students learned--among other things--to convert fractions into decimals in order to break a piece of code found in a library book; to use atlases and read maps to create a location guide for a reality television series; and to create video tutorials for a hapless group of fictional inventors. This research and development document outlines the learning framework for the school, making the original design available to others in the field. Elements in development include a detailed curriculum map, a budget, and samples of student and teacher handbooks. 410 0$aJohn D. and Catherine T. MacArthur Foundation Reports on Digital Media and Learning. 606 $aInformation technology$xStudy and teaching$zUnited States 606 $aComputers$xStudy and teaching$zUnited States 606 $aInternet in education$zUnited States 615 0$aInformation technology$xStudy and teaching 615 0$aComputers$xStudy and teaching 615 0$aInternet in education 676 $a371.33/44678 701 $aSalen$b Katie$0627079 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910141011303321 996 $aQuest to learn$92286071 997 $aUNINA LEADER 04917nam 22005895 450 001 9910728952503321 005 20251009085001.0 010 $a3-031-31004-7 024 7 $a10.1007/978-3-031-31004-1 035 $a(MiAaPQ)EBC30562368 035 $a(Au-PeEL)EBL30562368 035 $a(OCoLC)1381479818 035 $a(DE-He213)978-3-031-31004-1 035 $a(BIP)089626042 035 $a(CKB)26821622200041 035 $a(EXLCZ)9926821622200041 100 $a20230601d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aUnderstanding the Impact of Machine Learning on Labor and Education $eA Time-Dependent Turing Test /$fby Joseph Ganem 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (88 pages) 225 1 $aSpringerBriefs in Philosophy,$x2211-4556 311 08$aPrint version: Ganem, Joseph Understanding the Impact of Machine Learning on Labor and Education Cham : Springer,c2023 9783031310034 327 $aIntroduction: The difference between knowing and learning -- Labor Markets: Comparative learning advantages -- Learning to Work: The two dimensions of job performance -- The Judgment Game: The Turing Test as a general research framework -- The Learning Game: A time-dependent Turing Test -- Implications: Recommendations for future education and labor policies. 330 $aThis book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, ?learning algorithms??that enable machines to modify their actions based on real-world experiences?are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology. To explore the difference between knowing and learning, Turing?s ?Imitation Game,??that he proposed as a test for machine thinking?is expanded to include time dependence. The arguments presented in the book introduce three novel concepts: (1) Comparative learning advantage: This is a concept analogous to comparative labor advantagebut arises from the disparate times required to learn new knowledge bases/skillsets. It is argued that in the future, comparative learning advantages between humans and machines will determine their division of labor. (2) Two dimensions of job performance?expertise and interpersonal: Job tasks can be sorted into two broad categories. Tasks that require expertise have stable endpoints, which makes these tasks inherently repetitive and subject to automation. Tasks that are interpersonal are highly context-dependent and lack stable endpoints, which makes these tasks inherently non-routine. Humans compared to machines have a comparative learning advantage along the interpersonal dimension, which is increasing in value economically. (3) The Learning Game is a time-dependent version of Turing?s ?Imitation Game.? It is more than a thought experiment. The ?Learning Game? provides a mathematical framework with quantitative criteria for training and assessing comparative learningadvantages. The book is highly interdisciplinary?presenting philosophical arguments in economics, artificial intelligence, and education. It also provides data, mathematical analysis, and testable criteria that researchers in these fields will find of practical use. The book calls for a rethinking of how labor markets operate and how the education system should prepare students for future jobs. It concludes with a list of counterintuitive recommendations for future education and labor policies that all stakeholders?employers, employees, educators, students, and political leaders?should heed. 410 0$aSpringerBriefs in Philosophy,$x2211-4556 606 $aTechnology$xPhilosophy 606 $aArtificial intelligence 606 $aMachine learning 606 $aPhilosophy of Technology 606 $aArtificial Intelligence 606 $aMachine Learning 615 0$aTechnology$xPhilosophy. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 14$aPhilosophy of Technology. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 676 $a006.3101 700 $aGanem$b Joseph$01061194 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910728952503321 996 $aUnderstanding the Impact of Machine Learning on Labor and Education$93384499 997 $aUNINA