1.

Record Nr.

UNINA9910971407303321

Autore

Stainthorp Rhona

Titolo

Learning from children who read at an early age / / Rhona Stainthorp and Diana Hughes

Pubbl/distr/stampa

London ; ; New York, : Routledge, 1999

ISBN

1-134-68610-2

0-415-17494-5

1-134-68611-0

1-280-33619-6

0-203-20123-X

Edizione

[1st ed.]

Descrizione fisica

1 online resource (x, 179p. ) : ill., facsims

Altri autori (Persone)

HughesDiana

Disciplina

372.41/4

Soggetti

Reading readiness

Reading (Early childhood)

Language arts (Early childhood)

Literacy

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references (p. 167-171) and indexes.

Nota di contenuto

1. In the Beginning Reading and Writing 2. The Project 3. The Children 4. Reading 5. Writing 6. Teacher Interviews 7. Interviews with Parents 8. Child Interviews 9. Learning from Successful Readers

Sommario/riassunto

This work is the result of a three-year research project in which the authors studied a group of children who learnt to read without being taught.



2.

Record Nr.

UNINA9911028753303321

Autore

Garcia-Cardona Cristina

Titolo

Advances in Data Science : Women in Data Science and Mathematics (WiSDM) 2023

Pubbl/distr/stampa

Cham : , : Springer, , 2025

©2025

ISBN

3-031-87804-3

Edizione

[1st ed.]

Descrizione fisica

1 online resource (539 pages)

Collana

Association for Women in Mathematics Series ; ; v.37

Altri autori (Persone)

LeeHarlin

Disciplina

004.0151

Soggetti

Intel·ligència artificial

Teoria de grafs

Reconeixement de formes (Informàtica)

Enginyeria de programari

Dades massives

Teoria de la informació

Llibres electrònics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

This volume features recent advances in data science ranging from algebraic geometry used for existence and uniqueness proofs of low rank approximations for tensor data, to category theory used for natural language processing applications, to approximation and optimization frameworks developed for convergence and robustness guarantees for deep.