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Libros sobre el tema "Inference teaching"

Autor: Grafiati
Publicado: 2 de julio de 2021
Última modificación: 10 de febrero de 2022

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1

Spector, Cecile Cyril. Between the lines: Enhancing inferencing skills. Eau Claire, WI: Thinking Publications, 2006.

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2

Theorie und Training deduktiven Urteilens. Frankfurt am Main: P. Lang, 1989.

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3

Thomas, Dewing R. y Perini Matthew J. 1973-, eds. Inference: Teaching students to develop hypotheses, evaluate evidence, and draw logical conclusions : a strategic teacher PLC guide. Alexandria, VA: ASCD, 2012.

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4

David, Nash. Getting the picture: Inference and narrative skills for young people with communication difficulties. London: Jessica Kingsley Publishers, 2011.

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5

Wesche, Marjorie Bingham. Lexical inferencing in a first and second language: Cross-linguistic dimensions. Buffalo, N.Y: Multilingual Matters, 2009.

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6

DeGaetano, Jean Gilliam. E couter, comprendre et agir: Activite s pour de velopper les habilete s d'e coute, d'attention et de compre hension verbale. Montre al: Chenelie re/McGraw-Hill, 2003.

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7

Haastrup, Kirsten. Lexical inferencing procedures, or, Talking about words: Receptive procedures in foreign language learning with special reference to English. Tübingen: G. Narr, 1991.

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8

Freedle, Roy O. The prediction of TOEFL reading comprehension item difficulty for expository prose passages for three item types--main idea, inference, and supporting idea items. Princeton, N.J: Educational Testing Service, 1993.

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9

LoGiudice, Carolyn. Spotlight on listening comprehension: Making inferences. East Moline, IL: LinguiSystems, 2006.

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10

1961-, Green Donald P., ed. Field experiments: Design, analysis, and interpretation. New York: W. W. Norton, 2012.

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11

Jiménez, Francisca Suau. La inferencia léxica como estrategia cognitiva: Aplicación al discurso escrito en lengua inglesa. Valencia: Universitat de València, 2000.

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12

Jiménez, Francisca Suau. La inferencia léxica como estrategia cognitiva: Aplicación al discurso escrito en lengua inglesa. Valencia: Universitat de Valencia, Departamento de Filología Inglesa y Alemana, Facultad de Filología, 2000.

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13

VanInwagen, Patricia Y. Inference Is a Guess You Make. Zephyr Press, 1997.

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14

Reading Between the Lines: Understanding Inference. Routledge, 2011.

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15

(Editor), Micheline Chalhoub-Deville, Carol A. Chapelle (Editor) y Patricia Duff (Editor), eds. Inference And Generalizability in Applied Linguistics: Multiple Perspectives (Language Learning and Language Teaching). John Benjamins Publishing Co, 2006.

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16

(Editor), Micheline Chalhoub-Deville, Carol A. Chapelle (Editor) y Patricia Duff (Editor), eds. Inference And Generalizability in Applied Linguistics: Multiple Perspectives (Language Learning and Language Teaching). John Benjamins Publishing Co, 2006.

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17

Vasquez-Martinez, Claudio-Rafael. A Mathematics Pedagogy and Rhochrematic: Observations for an Inference. Editorial Papiro, 2003.

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18

Lisa, Towne, Shavelson Richard J y Feuer Michael J, eds. Science, evidence, and inference in education: Report of a workshop. Washington, DC: National Academy Press, 2001.

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19

(US), National Research Council y Committee on Scientific Principles in Education Research. Science, Evidence, and Inference in Education: Report of a Workshop. National Academies Press, 2001.

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20

Gelman, Andrew y Deborah Nolan. Teaching Statistics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.001.0001.

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Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, activities, examples and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and has chapters such as ‘First week of class’- with exercises to break the ice and get students talking; then descriptive statistics, graphics, linear regression, data collection (sampling and experimentation), probability, inference, and statistical communication. Part II gives tips on what works and what doesn’t, how to set up effective demonstrations, how to encourage students to participate in class and to work effectively in group projects. Course plans for introductory statistics, statistics for social scientists, and communication and graphics are provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics, sampling, and data science.
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21

Cordi, Jennifer. Engaging Knowledge: The Inference of Internet Content Development and Its Meaning for Scientific Learning and Research (Digital Learning, No. 1). ScarecrowEducation, 2004.

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22

Kacprzak, Agnieszka. Rhetoric and Roman Law. Editado por Paul J. du Plessis, Clifford Ando y Kaius Tuori. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780198728689.013.16.

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This chapter surveys the methods of constructing rational arguments taught in the schools of rhetoric and their impact on juridical argumentation. It surveys: the place of rhetoric in legal education; the basic tools of rhetorical invention, i.e. rhetorical syllogism and induction, general schemes of inference on which singular arguments depended (topoi), and types of questions on which court debates could concentrate (status); the difficulties one is likely to encounter when trying to identify traces of rhetorical teaching in legal sources. It is the contention of this chapter that such attempts are hardly successful, since rhetorical theory codifies, classifies, and to a lesser degree analyses types of argumentation people intuitively use, rather than create them. The mere fact that a jurist applied some pattern of reasoning as described in rhetorical handbooks is insufficient evidence to conclude either that he had some sort of rhetorical education or that he knew rhetorical theory.
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23

Edge, M. D. Statistical Thinking from Scratch. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198827627.001.0001.

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In virtually every field, researchers find themselves navigating tremendous amounts of new data. Making sense of this flood of information requires much more than the rote application of traditional statistical methods. This book will train researchers to be creative and confident users of statistics by thinking hard about the application of simple methods to a small dataset. In particular, this book focuses on simple linear regression—a method with strong connections to the most important tools in applied statistics—using it as a detailed case study for teaching resampling-based, likelihood-based, and Bayesian approaches to statistical inference. This exercise imparts an idea of how statistical procedures are designed and implemented, a flavor for the philosophical positions one implicitly assumes when applying statistics, and an opportunity to probe the strengths and weaknesses of one’s statistical approach. Key to the book’s novel approach is its mathematical level, which is gentler than most texts for statisticians but more rigorous than most introductory texts for non-statisticians. Statistical Thinking from Scratch is suitable for senior undergraduate and beginning graduate students, professional researchers, and practitioners seeking to improve their understanding of statistical methods across the natural and social sciences, medicine, psychology, public health, business, and other fields.
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24

Critical thinking skills: Context clues, inferences, classification, fact and opinion, problem solving. Torrance, Calif: Frank Schaffer Publications, 1995.

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