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Word Blocks Help Students Learn to Write Sentences

2000· article· en· W320549274 sur OpenAlexaboutno aff
Robert Ian Scott

Notice bibliographique

RevueETC.: A Review of General Semantics · 2000
Typearticle
Langueen
DomaineComputer Science
ThématiqueNatural Language Processing Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSentenceAdjectiveLinguisticsGrammarComputer scienceSubject (documents)VerbObject (grammar)Artificial intelligenceReading (process)Natural language processingNounPhilosophyWorld Wide Web
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

ROBERT IAN SCOTT [*] INTRODUCTION In 1957, Dr. Robert Ian Scott began teaching the grammar these blocks embody: Subject, Verb, Object, Qualifier (SVOQ). To help students learn to write clear and informative English, Dr. Scott devised some questions - mostly variations of the who? Does what? to whom? etc. of the SVOQ pattern. He often wrote these questions on the blackboard as patterns which provide recipes for writing sentences and for reading more perceptively. When he indicated choices of words in these patterns, he put them inside brackets [ ], and later in boxes. The boxes suggested the idea of putting the words on blocks. In 1968, Dr. Scott tested the grammar by putting words on actual blocks an seeing whether first-graders in Saskatoon would use the blocks to produce sentences, which they did with what he described as a delightful enthusiasm and intelligence. Because the blocks and the SVOQ grammar do not have the verb to be, they produce crisply specific E-Prime. Once, one child stopped using the blocks for moment and began sentence using is followed by an adjective. He then decided not to finish the sentence because, he said, the blocks didn't produce it, and the sentence didn't say what he wanted to say; he then went back to writing reports with active verbs. Such results suggest that the SVOQ and the blocks provide an independent confirmation of the validity of E-Prime and of structural grammars as experiments with practical applications. Teachers testing these blocks have made them by folding construction paper in the appropriate colors into cubes just under 3 x 3 x 3 inches in size. The teachers then wrote or typed the words on gummed address labels and put them on the blocks. Using Word Blocks to Produce Sentences THIS PROGRAM'S color-coded word blocks show six-year-olds (and older students) how to write sentences as variations of single one-word-after-another pattern of questions and answers, Subject-active Verb-Object-Qualifier, SVOQ for short: Who? does what? to whom? When, where, how? Subject Verb Object Qualifier(s) (red) (blue) (red) (yellow) The blocks make sentence patterns and their meanings more graphically clear and more easily handled. To use these blocks, arrange them in row, S, V, O, Q, and ask students to choose one word from each block in order to put sentences together. When these choices don't produce grammatical sentence, try other words or rearrange the blocks into another pattern. The blocks provide way for children to experiment, to make and test sentences, and so discover the many possibilities of our language. These possibilities include how we can rewrite any sentence into any other, as we 1) choose the appropriate words. Each side of each block gives us choice of words, or of one or more forms of the same word, as with see, sees, saw. 2) turn blocks over to substitute one word for another to produce sentences with different meanings, but the same pattern, as in Subject Verb Number Object We saw two dogs. Tom painted house. 3) cluster or uncluster. We cluster by putting more words into part of sentence to ask or answer more questions; we uncluster by removing words, making that part of the sentence shorter and also less specific, as in replacing the cluster those two very tall boys with the single word them. The longer the cluster, the more questions it can answer. 4) rearrange or add or remove blocks to transform (change) whole sentence patterns, as with putting the Q first: S V O Q We found weeds there. Q S V O There we found weeds. We also change sentence patterns by adding more answers, as in Where? In my garden, How many? we found hundreds of weeds. Instead of naming the words noun, verb, etc, see what happens when you or your students use various words in this or that place in sentence pattern. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,058
Score d'incertitude au seuil0,194

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0580,024

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,314
Écart entre enseignants0,301 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2000
Routes d'admission1
Résumé présentoui

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