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Record W320549274

Word Blocks Help Students Learn to Write Sentences

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

Bibliographic record

VenueETC.: A Review of General Semantics · 2000
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSentenceAdjectiveLinguisticsGrammarComputer scienceSubject (documents)VerbObject (grammar)Artificial intelligenceReading (process)Natural language processingNounPhilosophyWorld Wide Web
DOInot available

Abstract

fetched live from 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. …

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0580.024

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.314
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2000
Admission routes1
Has abstractyes

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