LEADER 05973oam 2200769M 450 001 9910783858603321 005 20240109165712.0 010 $a1-134-39574-4 010 $a0-415-30821-6 010 $a1-134-39575-2 010 $a1-280-06284-3 010 $a0-203-22099-4 024 7 $a10.4324/9780203220993 035 $a(CKB)1000000000250814 035 $a(EBL)181939 035 $a(OCoLC)57065734 035 $a(SSID)ssj0000299412 035 $a(PQKBManifestationID)12061550 035 $a(PQKBTitleCode)TC0000299412 035 $a(PQKBWorkID)10240819 035 $a(PQKB)11157434 035 $a(SSID)ssj0000376654 035 $a(PQKBManifestationID)11279456 035 $a(PQKBTitleCode)TC0000376654 035 $a(PQKBWorkID)10337458 035 $a(PQKB)11579795 035 $a(MiAaPQ)EBC181939 035 $a(Au-PeEL)EBL181939 035 $a(CaPaEBR)ebr10094520 035 $a(CaONFJC)MIL6284 035 $a(OCoLC)1055395219 035 $a(OCoLC-P)1055395219 035 $a(FlBoTFG)9780203220993 035 $a(EXLCZ)991000000000250814 100 $a20040604d2006 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aImproving Assessment Through Student Involvement $ePractical Solutions for Aiding Learning in Higher and Further Education 210 $aNew York $cRoutledge$dJune 2006$aFlorence $cTaylor & Francis Group [distributor] 215 $a1 online resource (305 p.) 300 $aDescription based upon print version of record. 311 $a0-203-35103-7 311 $a0-415-30820-8 320 $aIncludes bibliographical references (p. [256]-279) and indexes. 327 $aCover; Improving Assessment Through Student Involvement: Practical solutions for aiding learning in higher and further education; Copyright; Contents; Preface; Acknowledgements; Chapter 1 The seven pillars of assessment; 1 Why assess?; 2 How to assess?; 3 What to assess?; 4 When to assess?; 5 Who does the assessing?; 6 How well do we assess?; 7 Whither? What do we do or where do we go next?; Chapter 2 What's wrong with traditional assessment?; Limitations of assessment as measurement; Reliability and bias in teacher and examiner marking; Negative side effects of traditional assessment 327 $aThe relationship between traditional assessment and academic dishonestyThe role of the Internet in facilitating and detecting cheating; Advice to practitioners: strategies for preventing cheating; Summary; Appendix: Internet resources to help combat plagiarism; Chapter 3 Changing definitions of assessment; Assessment as measurement; Assessment as procedure; Assessment as enquiry; Assessment and accountability; Assessment as quality control; A synthesis of views; Problems associated with the four paradigms; Variations within the category of assessment as enquiry 327 $aKey differences between traditional and alternative assessmentsChapter 4 Why do teachers involve students in assessment?; Themes from the 1950s to 1980s; The 1990s: benefits and pressures; The early 2000s: the rise of the machines?; Overview; Summary: benefits of involving students in assessment; Chapter 5 How may students be involved in assessment?; Peer assessment; Self-assessment; Collaborative assessment; What is the level of student involvement?; What do students assess?; How are assessments carried out?; Recommendations to help combat bias in alternative assessments; Conclusion 327 $aChapter 6 Practical peer assessment and feedback: problems and solutionsFrequently asked questions; Peer feedback marking; Final comment; Chapter 7 How well are students able to judge their own work?; A qualitative review of self-assessment studies; A meta-analysis of student self-assessment studies; Recent self-assessment studies; Conclusion; Chapter 8 How reliable or valid are student peer assessments?; A meta-analysis of peer assessment studies; Results; Recommendations to practitioners for implementing peer assessment; Future work in this area; Chapter 9 Assessment of groups by peers 327 $aA survey of group peer assessment studiesProblems of peer assessment in groups; Strategies for designing and marking group assignments; Preparing for peer assessment in groups; Summary: recommendations for overcoming problems associated with peer assessment in groups; Chapter 10 Computer Assisted Assessment (CAA) and student involvement; What can be achieved by the use of CAA?; CAA and assessment of higher order skills; Functions of CAA; Summary: advice to practitioners; Chapter 11 Past, present and futures; References; Author index; Subject index 330 8 $aAnnotation$b"This book provides a scholarly account of the many facets of assessment, with a particular focus on student involvement. Peer and self-assessment are powerful assessment tools to add to the existing tutor-based methods of assessment and feedback, and this book is a comprehensive guide to the methods and issues involved." "Practical and accessible in style, yet grounded in research and rich in evidence-based material, Improving Assessment Through Student Involvement will be valued by all FE or HE professionals wanting to enhance both the effectiveness and quality of their assessment methods."--BOOK JACKET. Title Summary field provided by Blackwell North America, Inc. All Rights Reserved. 606 $aCollege students$xRating of 606 $aEducational tests and measurements 606 $aStudent participation in administration 615 0$aCollege students$xRating of. 615 0$aEducational tests and measurements. 615 0$aStudent participation in administration. 676 $a371.26 700 $aFalchikov$b Nancy$f1939-$01470669 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910783858603321 996 $aImproving Assessment Through Student Involvement$93682680 997 $aUNINA LEADER 04017nam 2200481 450 001 9910826656003321 005 20170926121532.0 035 $a(CKB)4100000000880849 035 $a(MiAaPQ)EBC5015713 035 $a(WaSeSS)IndRDA00090924 035 $a(CaSebORM)9781785283451 035 $a(PPN)228010705 035 $a(EXLCZ)994100000000880849 100 $a20170926h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aMastering machine learning with spark 2.x $ecreate scalable machine learning applications to power a modern data-driven business using spark /$fAlex Tellez, Max Pumperla, Michal Malohlava 205 $a1st edition 210 1$aBirmingham, England ;$aMumbai, [India] :$cPackt,$d2017. 210 4$dİ2017 215 $a1 online resource (320 pages) $cillustrations (some color) 300 $aIncludes index. 311 $a1-78528-345-6 311 $a1-78528-241-7 330 $aUnlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and ?small data? machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering... 606 $aMachine learning 606 $aMachine learning$xIndustrial applications 615 0$aMachine learning. 615 0$aMachine learning$xIndustrial applications. 676 $a006.31 700 $aTellez$b Alex$01663985 702 $aPumperla$b Max 702 $aMalohlava$b Michal 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826656003321 996 $aMastering machine learning with spark 2.x$94021731 997 $aUNINA