Executive Functioning and Figurative Language Comprehension in Learning Disabilities
Bibliographic record
Abstract
The goal of the research was to examine executive functioning and figurative language comprehension amongstudents with learning disabilities as compared to students without learning disabilities. As part of the research, weexamined 20 students with learning disabilities and 21 students with no learning disabilities, both groups of studentsattend 7th and 8th grade (ages 12-13). The participants were selected using: Glantz Abstract Verbal Thinking Test ofcognitive skills (2010), reading words with and without visual punctuation(Note 1) as well as a set of dummy words(Hadad, 2010). The level of executive functioning was evaluated using three types of assessment tools: a multiplemeaning word questionnaire, semantic fluency and phonetic fluency tests. The level of figurative speech wasevaluated using four assessment tools: idioms, colloquial expression, metaphor questionnaire and a pictorialmetaphor test.The development of language proficiency along with enhanced use of aural and visual metaphors among childrenwith learning disabilities, may contribute to improving the ability to plan, reinforce flexibility, bolster workingmemory and establish self monitor fluency; this in turn, builds up creative and abstract thinking as well as the abilityto understand and produce a metaphor. These findings and conclusions have implications on a variety ofpedagogical experiences including the reduction in school dropout rate, advancement of student achievementsthereby bolstering academic self esteem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".