HOW WELL DO THEY READ? BRIEF ENGLISH AND FRENCH SCREENING TOOLS FOR COLLEGE STUDENTS
Bibliographic record
Abstract
We translated and report on the psychometric properties of English and French versions of two reading difficulties screening tools for junior/community college students.We administered the Adult Reading History Questionnaire-Revised (ARHQ-R) (Parrila, Georgiou, & Corkett, 2007) to 1889 students enrolled in compulsory language courses in English and French colleges in the language of instruction at the school.Eighty students (4%) self-reported a learning disability.We also administered English and French versions of the Test de lecture -Épreuve de compréhension (Institut de Recherche et d'Évaluation Psychopédagogique Inc., 2000) to 432 college students.We categorized all participants into adequate, poor and very poor readers based on each measure and examined scores of students with and without learning disabilities.Although there were significant differences among groups under regular testing times, extended time resulted in comparable reading comprehension scores for all groups.Recommendations based on the findings are made for research and practice.Reading difficulties are a substantial concern in North American postsecondary education.Indeed, many students with specific learning disabilities (LDs) have reading difficulties.Furthermore, learning disabilities are amongst the most common disabilities in Canada's junior/community colleges (Lavallée, Raymond, & Savard, 2012).The Learning Disabilities Association of Canada (2002) defines LDs as a number of disorders which may affect the acquisition, organization, retention, understanding or use of verbal or nonverbal information in individuals with otherwise normal intellectual abilities.The definition notes that LDs may interfere with the acquisition and use of one or more of the following: oral language (e.g.listening, speaking, understanding); reading (e.g.decoding, phonetic knowledge, word recognition, comprehension); written language (e.g.spelling and written expression); and mathematics (e.g.computation, problem solving).( Learning Disabilities Association of Canada, 2002, paragraph 3) A specific type of learning disability (LD), known as dyslexia, is manifested in reading difficulties related to decoding problems and comprehension difficulties (Couston, 2006).This is the most common learning disability everywhere, including Québec (Mimouni & King, 2007;AQICESH, 2011).However, not all students who have poor readings abilities have an LD.Some students who have difficulty reading have not yet mastered the language of instruction.Other poor readers may, in fact, have an undiagnosed LD (Harrison, Larochette, & Nichols, 2007;Mimouni, 2006).Another explanation may be that reading skills are normally distributed in the population.Copie de conservation autorisée par l'ayant droit -Centre de documentation collégiale, disponible sur le serveur Web:
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".