Reading Speed of Contracted French Braille
Notice bibliographique
Résumé
Reading is essential in the context of education. For individuals who do not have easy access to print materials because they are visually impaired (that is, they are blind or have low vision), this process of acquiring knowledge through reading requires additional effort and accommodations. One key adjustment that is made for students for whom is the preferred communication method (hereafter referred to as braille readers) for completing their examinations is the allocation of additional time. Depending on the country, educational system, or institution, the amount of extra time that is may vary; however, in Quebec, Canada, the Ministry of Education, Leisure and Sport (MELS) has regulated the additional time allocated for readers by limiting the extension of the duration of the test to a maximum additional time equivalent of one third the time normally allotted (Gouvernement du Quebec, 2007, chap. 5, p. 55). It has been our experience that this allotment of time is not sufficient to allow students who are visually impaired to operate under the same time constraints as their sighted peers. To propose a concrete change in the amount of time, however, empirical data were necessary. The most easily measured component of an examination that is conducted using is reading speed, often recorded in words per minute (wpm). There are considerable individual differences in reading speed for both print and readers. Legge, Madison, and Mansfield (1999) used both the print and versions of the MNRead test to compare the reading speeds of sighted print readers and readers while reading out loud. Whereas print readers ranged in speed from 150 to 310 wpm (median = 251 wpm), readers ranged from 24 to 232 wpm (median = 124 wpm), indicating that some of the fast readers actually outperformed some of the slower print readers. Still, the median speed was approximately twice as fast for the print readers. These data would indicate that as far as reading is concerned, the time allotment for readers should be twice the time allocated for print readers. This logic does not hold, however, because most students do not take examinations orally. Therefore, a comparison of reading speeds for print and readers was necessary to investigate the difference in reading speed when reading silently. An additional aspect that makes a comparison of reading speeds difficult is the level of in which the text is written (contracted versus uncontracted) and in which language the text is transcribed. Specifically, the language is of importance in contracted because the demands on the reader differ across languages. A reader of English has to learn uncontracted as well as 189 contractions and short-form words to decode text in contracted (Braille Authority of North America, 2008). In comparison, because of differences in the French alphabet and language structure, a reader of French has to learn 1,168 contractions, divided into four levels in Qurbec, to be able to decode a text in contracted (braille abrege) (Gouvernement du Qurbec, 1997). This considerable difference in the complexity of contracted indicates that the cognitive load for readers of French is substantially higher that that of readers of English braille; however, these differences are not reflected in accommodations for students who use French during examinations. Furthermore, a student's reading speed in can be influenced by the reading technique that the student uses. It is now generally accepted that the majority of efficient and fast readers adopt a two-handed scissors pattern, whereby the left reading finger reads to the center of a line, at which point the right takes over and the left is free to find the beginning of the next line (Wright, Wormsley, & Kamei-Hannan, 2009). …
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».