03652nam 2200565 450 991081907010332120240130143647.01-4833-6024-51-4833-6243-4(CKB)3710000000456221(EBL)1651167(OCoLC)922907718(SSID)ssj0001531599(PQKBManifestationID)12647966(PQKBTitleCode)TC0001531599(PQKBWorkID)11464386(PQKB)11109176(MiAaPQ)EBC1651167(EXLCZ)99371000000045622120150818h20042004 uy 0engur|n|---|||||txtccrWhat every teacher should know about effective teaching strategies /Donna Walker Tileston ; indexer, Will Ragsdale ; cover designer, Tracy E. MillerThousand Oaks, California :Corwin Press,2004.©20041 online resource (137 p.)What every teacher should know about-Description based upon print version of record.0-7619-3121-X Includes bibliographical references and index.""Cover""; ""Contents""; ""About the Author""; ""Acknowledgments""; ""Introduction""; ""Vocabulary Pre-Test""; ""Chapter 1 - Making Good Decisions about Instructional Strategies""; ""How Do We Implement the Objectives?""; ""Why the Way We Teach is Important""; ""The Linguistic Modality""; ""The Nonlinguistic Modality""; ""The Affective Modality""; ""Semantic Memory""; ""Episodic Memory""; ""Procedural Memory""; ""Automatic Memory""; ""Emotional Memory""; ""Chapter 2 - Choosing Effective Teaching Strategies for Beginning Activities""; ""Great Beginnings""; ""The Self-System""""Chapter 3 - Working with Declarative Information: Teaching for Meaning""""Constructing Meaning from Declarative Information""; ""Organizing Declarative Information""; ""Storing Declarative Information""; ""Using the Semantic Memory for Storing Declarative Information""; ""Making Good Choices for Declarative Information""; ""Declarative Goals: Vocabulary""; ""Declarative Goals: Facts""; ""Declarative Goals: Using Sequences""; ""Declarative Goals: Understanding the Order of Events""; ""Declarative Goals: Organizing Data or Ideas""; ""Declarative Goals: Teaching Details""; ""Episodic Memory""""Procedural Memory""""Automatic Memory""; ""Emotional Memory""; ""Chapter 4 - Procedural Knowledge: Teaching Strategies That Work""; ""Helping Students Construct Models""; ""Teaching Procedural Knowledge That Requires Algorithms""; ""Strategies to Help Construct Models""; ""Shaping Procedural Knowledge""; ""Internalizing Procedural Knowledge""; ""The Metacognitive System""; ""Chapter 5 - Graphic Organizers: Strategies for Thinking""; ""Graphic Organizers""; ""Chapter 6 - Using Verbal Strategies in the Classroom""; ""Brainstorming""; ""Socratic Questioning""; ""Quaker Dialogues""""Real-World Application of the Learning""""Chapter 7 - Anatomy of a Lesson""; ""General Guidelines for Lessons""; ""Vocabulary Summary""; ""Vocabulary Post-Test""; ""References""; ""Index""Effective teachingEffective teaching.371.102Tileston Donna Walker1622181Ragsdale WillMiller TracyMiAaPQMiAaPQMiAaPQBOOK9910819070103321What every teacher should know about effective teaching strategies3999439UNINA04219nam 22005895 450 991088109380332120240815130225.03-031-60339-710.1007/978-3-031-60339-6(MiAaPQ)EBC31605063(Au-PeEL)EBL31605063(CKB)34039650500041(DE-He213)978-3-031-60339-6(EXLCZ)993403965050004120240815d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierStatistical Learning Tools for Electricity Load Forecasting /by Anestis Antoniadis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, Jean-Michel Poggi1st ed. 2024.Cham :Springer International Publishing :Imprint: Birkhäuser,2024.1 online resource (232 pages)Statistics for Industry, Technology, and Engineering,2662-55633-031-60338-9 Introduction -- Part I: A Toolbox of Models -- Additive Modelling of Electricity Demand with mgcv -- Probabilistic GAMs: Beyond Mean Modelling -- Functional Time Series -- Random Forests -- Aggregation of Experts -- Mixed Effects Models for Electricity Load Forecasting -- Part II: Case Studies: Models in Action on Specific Applications -- Disaggregated Forecasting of the Total Consumption -- Aggregation of Multi-Scale Experts -- Short-Term Load Forecasting using Fine-Grained Data -- Functional State Space Models -- Forecasting Daily Peak Demand using GAMs -- Forecasting During the Lockdown Period.This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives – generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.Statistics for Industry, Technology, and Engineering,2662-5563StatisticsMachine learningStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesStatistical LearningMachine LearningStatistics.Machine learning.Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.Statistical Learning.Machine Learning.519Antoniadis Anestis737862Cugliari Jairo1765524Fasiolo Matteo1765525Goude Yannig1765526Poggi Jean-Michel1253641MiAaPQMiAaPQMiAaPQBOOK9910881093803321Statistical Learning Tools for Electricity Load Forecasting4207210UNINA