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Enregistrement W2338289468 · doi:10.14288/1.0100946

Processes and strategies used by normal and disabled readers in analogical reasoning

2011· article· en· W2338289468 sur OpenAlexaboutno aff
Margaret A. Potter

Notice bibliographique

RevuecIRcle (University of British Columbia) · 2011
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation and Critical Thinking Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAnalogical reasoningComputer scienceNatural language processingArtificial intelligenceLinguisticsAnalogy

Résumé

récupéré en direct d'OpenAlex

The purpose of this study was: 1) to identify reading disability subtypes among a sample of reading-disabled students using two classification methods, 2) to discover the processes and strategies used in analogical reasoning by individual reading disabled and nonreading-disabled students through the method of componential analysis, and 3) to explore the relationship between the processes and strategies used by disabled readers in analogical reasoning and their membership in a reading disability subtype. In Phase 1 of the study, groups of normal and disabled readers were established using Grade 5 students attending elementary schools in a large urban area of Northwestern Ontario. The disabled sample of 77 students comprised 41 males and 36 females and the normal reader sample of 20 students comprised 7 males and 13 females. In Phase 2, the disabled and normal readers were individually administered the Boder Test of Reading-Spelling Patterns (Boder & Jarrico, 1982), the Peabody Picture Vocabulary Test - Revised (Dunn & Dunn, 1981), and subtests taken from the Durrell Analysis of Reading Difficulty (Durrell & Catterson, 1980) . The Schematic Picture Analogies Test (Sternberg & Rifkin, 1979) was administered to students in small groups. The first method of subtyping, the Boder test, failed to identify subtypes among the reading-disabled sample because the students were not as severely disabled as the clinic-referred sample for which the test was designed. The second method, which employed a hierarchical agglomerative technique of cluster analysis using students' scores obtained on 23 reading and related variables, differentiated the normal readers from the disabled readers. Three clusters emerged when the reading-disabled data were analyzed alone that were characterized by strengths and weaknesses in their reading skills. Componential analysis of students' analogical reasoning data used mean solution latency as the criterion or dependent variables. Independent or predictor variables were associated with the systematically varied level of difficulty of each of 24 analogy booklets. Seven models theorized by Sternberg (1977) were fitted to each individual's booklet scores through multiple regression analysis and the preferred model chosen according to five predetermined criteria (Sternberg & Rifkin, 1979). Disabled readers were grouped according to the processes and strategies they used in solving analogies. The normal reader group solved analogies as predicted but there was no relationship between membership in a reading disability cluster and membership in an analogy subgroup. None of the analogy subgroups could be characterized by their reading performance although the subgroup that used the most efficient model tended to have higher ability than the other subgroups. Correlations between solution latency and reading and related variables for the normal readers showed that the more proficient analogical reasoners were faster, more accurate readers and better comprehenders. Few significant correlations were detected between solution latency and reading variables for the disabled readers. The lack of relationship between the two systems is perhaps the most surprising and paradoxical finding of the study. It is suggested that this occurred because reading-disabled children, irrespective of the cluster to which they belong, may solve analogies in a unique way, or because the bottom-up, content-driven nature of the reading task is so fundamentally different from the top-down, content-free nature of the analogical reasoning task. Other explanations suggest that the use of measures at a macro level to form reading-disabled clusters masks any relationship with the analogical reasoning subgroups formed by measures at a micro level, or that component processing is so specific to the individual that differences are buried within the subtypes implying the existence of subtypes within subtypes. Some of the implications for education are discussed.

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)0,014
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,014
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,024
Tête enseignante GPT0,227
Écart entre enseignants0,203 · 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
Publié2011
Routes d'admission1
Résumé présentoui

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