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Record W2338289468 · doi:10.14288/1.0100946

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

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

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsAnalogical reasoningComputer scienceNatural language processingArtificial intelligenceLinguisticsAnalogy

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.227
Teacher spread0.203 · 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
Published2011
Admission routes1
Has abstractyes

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