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Record Nr. |
UNINA9910810322703321 |
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Titolo |
Using evidence of student learning to improve higher education / / George D. Kuh [and six others] |
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Pubbl/distr/stampa |
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San Francisco, California : , : National Institute for Learning Outcomes Assessment : , : Jossey-Bass, , 2015 |
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©2015 |
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ISBN |
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1-118-90366-8 |
1-118-90373-0 |
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Descrizione fisica |
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1 online resource (304 p.) |
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Classificazione |
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Disciplina |
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Soggetti |
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Education, Higher - Aims and objectives - United States |
Educational tests and measurements - United States - Evaluation |
Universities and colleges - United States - Evaluation |
Educational change - United States |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Machine 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. |
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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 . |
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Sommario/riassunto |
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"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"-- |
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2. |
Record Nr. |
UNINA9910437926303321 |
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Titolo |
Machine learning for computer vision / / Roberto Cipolla, Sebastiano Battiato, and Giovanni Maria Farinella (eds.) |
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Pubbl/distr/stampa |
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Berlin ; ; New York, : Springer, c2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (XXII, 250 p.) |
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Collana |
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Studies in computational intelligence, , 1860-949X ; ; 411 |
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Altri autori (Persone) |
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CipollaRoberto |
BattiatoSebastiano |
FarinellaGiovanni Maria |
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Disciplina |
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Soggetti |
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Computer vision |
Machine learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Throwing Down the Visual Intelligence Gauntlet -- Actionable Information in Vision -- Learning Binary Hash Codes for Large-Scale Image Search -- Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition -- Real-Time Human Pose Recognition in Parts from Single Depth Images -- Scale-Invariant Vote- |
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based 3D Recognition and Registration from Point Clouds -- Multiple Classifier Boosting and Tree-Structured Classifiers -- Simultaneous detection and tracking with multiple cameras -- Applications of Computer Vision to Vehicles: an extreme test. |
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Sommario/riassunto |
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Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature. |
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