A STUDY OF FACTORS AFFECTING THE ADOPTION OF CURRICULUM
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".