Distance learning with a personalized system of instruction
Notice bibliographique
Résumé
Distance learning is growing everywhere. In Brazil distance learning courses are becoming more common and educational institutions are authorized to develop distance learning programs. Nevertheless, distance learning courses as any other teaching procedures, will be effective only if teaching contingencies are carefully planned and implemented. Behavior analysis as a discipline has accumulated technology that is suitable for distance learning, based on the Personalized System of Instruction (PSI), first developed by Keller, in 1968. PSI courses are characterized by: course content is broken down in small units, learning goals are previously established, studying pace depends on the student, mastery is a requisite on each unit, emphasis on written material, immediate feedback for students, proctors. A distance learning program that uses the internet, called Computer-aided Personalized System of Instruction (CAPSI), developed at the University of Manitoba, Canada, some 20 years, has been applied to many disciplines with promising results. CAPSI courses have all the characteristics of PSI courses and are taken by students through the internet. The system makes tests and exams available to the student, records students performances and progress, and manages aspects of the course, such as sending tests for correction. Tests are taken when students apply for them and are marked by teachers, instructors and/or proctors (advanced students). Tests become eligible when students master previous tests. This study was conducted to test the generality of previous research on CAPSI with Brazilian students, with a course on Behavior Analysis Principles. 77 students (62 from the same teaching institution) and the others from other state were enrolled, but only for 33 of them the course was initially mandatory (as part of their professional training). The mandatory status was changed on the 9th week of the course. The following variables are considered as the course results: dropouts, students performances on tests and exams, level of difficulty of tests, students activities as proctors, precision and content of feedbacks to students, feedback effects, and students assessment of the course. Results showed a larger number of dropouts when compared with the literature. Other results are consistent with the literature: students grades are high and students evaluation of the course is similar to those reported previously. The higher percentage of dropouts is discussed as s a probable function of students previous history as well as the elective character of the course
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».