LEADER 01930nam 2200661Ia 450 001 9910458034603321 005 20200520144314.0 010 $a0-299-05403-9 010 $a1-282-62278-1 010 $a9786612622786 035 $a(CKB)1000000000396255 035 $a(dli)HEB01348 035 $a(SSID)ssj0000083829 035 $a(PQKBManifestationID)11126294 035 $a(PQKBTitleCode)TC0000083829 035 $a(PQKBWorkID)10163068 035 $a(PQKB)10726112 035 $a(SSID)ssj0000412302 035 $a(PQKBManifestationID)11306241 035 $a(PQKBTitleCode)TC0000412302 035 $a(PQKBWorkID)10367321 035 $a(PQKB)11697872 035 $a(MiAaPQ)EBC3445002 035 $a(OCoLC)55718172 035 $a(MdBmJHUP)muse13485 035 $a(Au-PeEL)EBL3445002 035 $a(CaPaEBR)ebr10394931 035 $a(CaONFJC)MIL262278 035 $a(OCoLC)748354956 035 $a(EXLCZ)991000000000396255 100 $a20691210d1969 uy 0 101 0 $aeng 135 $aurmnummmmuuuu 181 $ctxt 182 $cc 183 $acr 200 14$aThe Atlantic slave trade$b[electronic resource] $ea census /$fby Philip D. Curtin 210 $aMadison $cUniversity of Wisconsin Press$d1969 215 $a1 online resource (xix, 338 p. ) $cmaps ; 300 $aIncludes index. 311 $a0-299-05404-7 311 $a0-299-05400-4 320 $aIncludes bibliographical references and index. 410 0$aACLS Humanities E-Book. 606 $aSlave trade$xHistory 606 $aSlavery$xHistory 608 $aElectronic books. 615 0$aSlave trade$xHistory. 615 0$aSlavery$xHistory. 676 $a382/.44/09 700 $aCurtin$b Philip D$0133933 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910458034603321 996 $aThe Atlantic slave trade$92472478 997 $aUNINA LEADER 04088nam 2200649 450 001 9910787119503321 005 20230807212248.0 010 $a1-118-90366-8 010 $a1-118-90373-0 035 $a(CKB)3710000000315819 035 $a(EBL)1882234 035 $a(SSID)ssj0001404033 035 $a(PQKBManifestationID)12593580 035 $a(PQKBTitleCode)TC0001404033 035 $a(PQKBWorkID)11380512 035 $a(PQKB)11498160 035 $a(PQKBManifestationID)16039621 035 $a(PQKB)22410802 035 $a(MiAaPQ)EBC1882234 035 $a(DLC) 2015001498 035 $a(Au-PeEL)EBL1882234 035 $a(CaPaEBR)ebr10997828 035 $a(OCoLC)900194574 035 $a(EXLCZ)993710000000315819 100 $a20150106h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aUsing evidence of student learning to improve higher education /$fGeorge D. Kuh [and six others] 210 1$aSan Francisco, California :$cNational Institute for Learning Outcomes Assessment :$cJossey-Bass,$d2015. 210 4$dİ2015 215 $a1 online resource (304 p.) 300 $aDescription based upon print version of record. 311 $a1-118-90339-0 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: Preface ix Acknowledgments xvii About the Authors xix 1. From Compliance to Ownership: Why and How Colleges and Universities Assess Student Learning 1 Stanley O. Ikenberry and George D. Kuh PART ONE What Works? Finding and Using Evidence 2. Evidence of Student Learning: What Counts and What Matters for Improvement 27 Pat Hutchings, Jillian Kinzie, and George D. Kuh 3. Fostering Greater Use of Assessment Results: Principles for Effective Practice 51 Jillian Kinzie, Pat Hutchings, and Natasha A. Jankowski 4. Making Assessment Consequential: Organizing to Yield Results 73 Jillian Kinzie and Natasha A. Jankowski PART TWO Who Cares? Engaging Key Stakeholders 5. Faculty and Students: Assessment at the Intersection of Teaching and Learning 95 Timothy Reese Cain and Pat Hutchings 6. Leadership in Making Assessment Matter 117 Peter T. Ewell and Stanley O. Ikenberry 7. Accreditation as Opportunity: Serving Two Purposes with Assessment 146 Peter T. Ewell and Natasha A. Jankowski 8. The Bigger Picture: Student Learning Outcomes Assessment and External Entities 160 Jillian Kinzie, Stanley O. Ikenberry, and Peter T. Ewell PART THREE What Now? Focusing Assessment on Learning 9. Assessment and Initiative Fatigue: Keeping the Focus on Learning 183 George D. Kuh and Pat Hutchings 10. From Compliance Reporting to Effective Communication: Assessment and Transparency 201 Natasha A. Jankowski and Timothy Reese Cain 11. Making Assessment Matter 220 George D. Kuh, Stanley O. Ikenberry, Natasha A. Jankowski, Timothy Reese Cain, Peter T. Ewell, Pat Hutchings, and Jillian Kinzie References 237 Appendix A: NILOA National Advisory Panel 261 Appendix B: NILOA Staff, 2008 to 2014 263 Index 265 . 330 $a"Offers a fresh and strategic approach to the processes by which evidence about student learning is obtained and used to inform efforts to improve teaching, learning, and decision-making"--$cProvided by publisher. 606 $aEducation, Higher$xAims and objectives$zUnited States 606 $aEducational tests and measurements$zUnited States$xEvaluation 606 $aUniversities and colleges$zUnited States$xEvaluation 606 $aEducational change$zUnited States 615 0$aEducation, Higher$xAims and objectives 615 0$aEducational tests and measurements$xEvaluation. 615 0$aUniversities and colleges$xEvaluation. 615 0$aEducational change 676 $a378.73 686 $aEDU015000$2bisacsh 702 $aKuh$b George D. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910787119503321 996 $aUsing evidence of student learning to improve higher education$93790824 997 $aUNINA LEADER 02803oam 2200649zu 450 001 9910220099403321 005 20210807000941.0 010 $a0-8330-8697-9 035 $a(CKB)2560000000315374 035 $a(SSID)ssj0001542574 035 $a(PQKBManifestationID)16131417 035 $a(PQKBTitleCode)TC0001542574 035 $a(PQKBWorkID)14352360 035 $a(PQKB)11251146 035 $a(oapen)doab115352 035 $a(EXLCZ)992560000000315374 100 $a20160829d2014 uy 101 0 $aeng 135 $aurmn|---annan 181 $ctxt 182 $cc 183 $acr 200 10$aPreparing principals to raise student achievement : implementation and effects of the New Leaders program in ten districts 210 $cRAND Corporation$d2014 210 31$a[Place of publication not identified]$cRand Corporation$d2014 215 $a1 online resource 225 0 $aRR-507-NL Preparing principals to raise student achievement 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a0-8330-8640-5 327 $aResearch methods -- Overview of the new leaders program and district partnership approach -- New leaders partnerships -- Analysis of impacts on student achievement -- Factors Associated with new leaders program effects -- Conclusion. 330 $aNew Leaders is a nonprofit organization that partners with school districts to prepare and support principals. This report describes how the New Leaders program was implemented in partner districts, and it provides evidence of the effect that New Leaders has on student achievement. 606 $aSchool principals$xTraining of$zUnited States$vCase studies 606 $aSchool management and organization$zUnited States$vCase studies 606 $aEducational leadership$zUnited States$vCase studies 606 $aEducation, Urban$zUnited States$vCase studies 606 $aAcademic achievement$zUnited States$vCase studies 606 $aTheory & Practice of Education$2HILCC 606 $aEducation$2HILCC 606 $aSocial Sciences$2HILCC 607 $aUnited States$2fast 608 $aCase studies.$2fast 615 0$aSchool principals$xTraining of 615 0$aSchool management and organization 615 0$aEducational leadership 615 0$aEducation, Urban 615 0$aAcademic achievement 615 7$aTheory & Practice of Education 615 7$aEducation 615 7$aSocial Sciences 676 $a371.2/012 700 $aGates$b Susan M.$f1968-$0935292 712 02$aRand Education (Institute), 801 0$bPQKB 906 $aBOOK 912 $a9910220099403321 996 $aPreparing principals to raise student achievement : implementation and effects of the New Leaders program in ten districts$92882763 997 $aUNINA LEADER 05194nam 22005775 450 001 9910410040003321 005 20251116224342.0 010 $a3-030-40344-0 024 7 $a10.1007/978-3-030-40344-7 035 $a(CKB)4100000011273812 035 $a(MiAaPQ)EBC6195866 035 $a(DE-He213)978-3-030-40344-7 035 $a(PPN)248397176 035 $a(EXLCZ)994100000011273812 100 $a20200512d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLinear Algebra and Optimization for Machine Learning $eA Textbook /$fby Charu C. Aggarwal 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (507 pages) $cillustrations 311 08$a3-030-40343-2 320 $aIncludes bibliographical references and index. 327 $aPreface -- 1 Linear Algebra and Optimization: An Introduction -- 2 Linear Transformations and Linear Systems -- 3 Eigenvectors and Diagonalizable Matrices -- 4 Optimization Basics: A Machine Learning View -- 5 Advanced Optimization Solutions -- 6 Constrained Optimization and Duality -- 7 Singular Value Decomposition -- 8 Matrix Factorization -- 9 The Linear Algebra of Similarity -- 10 The Linear Algebra of Graphs -- 11 Optimization in Computational Graphs -- Index. 330 $aThis textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The ?parent problem? of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning. 606 $aMachine learning 606 $aMatrix theory 606 $aAlgebra 606 $aComputers 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aLinear and Multilinear Algebras, Matrix Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M11094 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 615 0$aMachine learning. 615 0$aMatrix theory. 615 0$aAlgebra. 615 0$aComputers. 615 14$aMachine Learning. 615 24$aLinear and Multilinear Algebras, Matrix Theory. 615 24$aInformation Systems and Communication Service. 676 $a512.5 700 $aAggarwal$b Charu C.$4aut$4http://id.loc.gov/vocabulary/relators/aut$0518673 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910410040003321 996 $aLinear Algebra and Optimization for Machine Learning$91959986 997 $aUNINA