A STUDY OF FACTORS AFFECTING THE ADOPTION OF CURRICULUM
Notice bibliographique
Résumé
The purpose of this study was to investigate factors affecting the adoption of the current home economics curriculum in Division IV in the province of Saskatchewan. The factors to be investigated were: teacher age, experience, academic qualifications as reflected by degrees, total number of home economics classes at the university level, total number of University of Saskatchewan home economics classes in a specific area of instruction and school enrollments. Data for the study were collected from a number of sources and were punched onto computer cards and analyzed at the Computing Services Center at the University of Saskatchewan. Descriptive statistics were used to describe the characteristics of the population. The second part of the analysis involved an investigation of the relationship between the decision to adopt and the selected factors. Percentage cross tabulations were used to describe these relationships. The chi square statistic was utilized and the 5 percent level chosen as the accepted level of significance. When the chi square analysis indicated there was an association, then the corrected coefficient of contingency was utilized to estimate the magnitude of the relationship and an interpretation of the substantive importance of this measure was built into the study. The population consisted of all Saskatchewan Division IV home economics teachers teaching one-third time or more for the school years 1970-1971, 1971-1972, 1972-1973 (N = 254). The population was found to: range from 20 to 65 years of age; range from 0 to 37 years of teaching experience; have approximately 40 percent teaching with no university degrees; have approximately 40 percent teaching without a major in home economics and approximately one-third without a single university class in home economics; have approximately 50 percent teaching with no university classes in at least one of the three major areas of Foods and Nutrition, Clothing and Textiles, and Housing and Design; be teaching in schools with enrollments ranging from 54 to 1,785 students. Significant relationships were found to exist in the analysis of the three curricula (Advanced Foods I, Advanced Clothing I, and Housing and Design) for all factors except Factor 2, teacher age. For this factor a significant relationship (p ≤ .01) was found to exist for two of the three curricula studied and these were interpreted as strong relationships. This analysis did not support the theory that the older the person the more resistance there is to change. In the analysis of: Factor 1, teaching experience, the group with under three years of experience had the largest percentage of non—adopters; Factor 3, academic qualifications, the B.S.H.Ec. + B.Ed. group had the largest percentage of both adopters and innovators while the group with an unrelated degree (a B.A. or a B.Sc.) or no degree had the largest percentage of non—adopters; Factor 4, university home economics classes, the groups with a teaching major had the highest percentage of adopters and innovators and the group with no classes had the largest percentage of non-adopters; Factor 5, university home economics classes in a specific area of instruction, it was found that as the number of classes increased, so did the number of both adoptions and innovations; Factor 6, school enrollments, it was found that as school size increased, so did the number of both adoptions and innovations. Profiles of non-adopters, adopters and innovators are provided to assist in the recruitment and placement of teachers.
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 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 | 0,002 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».