Teaching individuals to conduct a preference assessment procedure using computer-aided personalized system of instruction
Notice bibliographique
Résumé
Preference assessments are an evidence-based procedures used to identify potential reinforcers for persons with developmental disabilities. There is a need to develop effective and efficient procedures to teach students and staff to conduct preference assessments, but only a small number of studies have been conducted and only two have used self-instructional materials. A recent study by Ramon et al. (2012) found that a self-instructional manual was more effective than a method description extracted from published articles for teaching university students to conduct multiple-stimulus without replacement preference assessments for persons with developmental disabilities. The present study extended this research by (a) adapting the self-instructional manual from Ramon et al. for online delivery, (b) adding video modeling as a teaching component, and (c) delivering the training package using a modified computer-aided personalized system of instruction (CAPSI, Pear and Kinsner, 1988). The training package was evaluated using a multiple-baseline design across three university students, replicated across three more students; and a multiple-baseline design across a pair of staff members, replicated a across a second pair. During the baseline phase, participants studied a two-page written description of the assessment procedure adapted from published studies. During the self-instructional manual phase, participants completed all of the following online: studied the self-instructional manual presented in eight units, viewed video demonstrations of the procedure, and completed review exercises scored by the computer program to demonstrate mastery of each study unit. Performance accuracy of each participant was scored using a standard behaviour checklist during a simulated preference assessment conducted following each phase. Clear and immediate improvement in performance accuracy was observed in all participants immediately following the self-instructional training package. Overall, students improved from a mean of 35% correct in baseline to a mean of 94% correct following CAPSI and staff improved from a mean of 23% correct in baseline to a mean of 87% correct following CAPSI. During retention and generalization assessments conducted from 7 to 17 days following self-instructional training, five of the six students and one of the four staff members performed at or above 85% correct (the mastery criterion). The findings showed that online delivery of the self-instructional manual plus video modeling has tremendous potential for providing an effective method for teaching a preference assessment procedure without face-to-face instruction.
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 | 0,000 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».