The effects of enhanced word processing on the journal writing of middle school students with learning disabilities
Notice bibliographique
Résumé
The purpose of this investigation was to examine whether the use of word processors, enhanced with speech synthesis and word prediction software, assisted the transcription skills and word recognition abilities of Middle School students with learning disabilities, while writing dialogue journals. An ABAB single-case research design, with probes, was implemented in an inclusive classroom setting with four students, ages eleven and twelve, with learning disabilities and severe writing problems. Effects of the intervention were examined during baseline sessions, when the students utilized a regular word processor, and during the treatment sessions, when the students used an enhanced word processor. The results were analyzed to find what effect, if any, the intervention, using a word processor enhanced with speech synthesis and word prediction software when dialogue journal writing, had on (a) the number of words in the students' dialogue journal writings, (b) the transcription quality of these students' dialogue journal entries, (c) the number of words recognized by the students when reading their dialogue journal entries, ( d) the students' composing rates, ( e) the students' transcription and word recognition skills when writing their subsequent journal writings on word processors (without enhancements) in the classroom, (f) the students' transcription and word recognition skills when writing their dialogue journals on word processors (without enhancements) one week and three weeks after the second treatment phase, and (g) the students' transcription skills when writing (by hand) in their daily agenda books. Data were analyzed on an individual basis and across participants. None of the participants improved the quantity of words they wrote when using an enhanced word processor. Three of the four participants' word quantity remained constant and one participant decreased the number of words he/she was able to write during the fifteen-minute sessions. All of the participants showed enhanced transcription skills, that is, the proportions of readable words, correctly spelled words, readable word sequences, and correctly spelled word sequences when they wrote their dialogue journals on the enhanced word processor. The word recognition baseline session scores for all the participants were near, or at the ceiling; consequently, treatment effect was minimal. Two of the four students' composing rates decreased when they composed their dialogue journals on the enriched word processor; whereas, the other two participants' composing rates were unaffected by the treatment. Using an enhanced word processor did not have any effect on the students' transcription skills when writing subsequent dialogue journal writings on word processors (without enhancements) or when writing (by hand) in their daily agenda books. Recommendations for future research included identifying (a) other populations and age groups that might benefit from an enhanced word processor, (b) other types of writing tasks that might be supported by using an enhanced word processor, (c) other academic areas that may benefit from use of an enhanced word processor such as spelling skills, (d) and whether or not using an enhanced word processor when dialogue journal writing over a longer period of time would affect the students' transcription skills when using a regular word processor. A recommendation for practice suggested that Middle School teachers use assistive technology to aid the writing skills of students with learning disabilities.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».