Learning communities, achievement and completion: Exploring relationships in southern Alberta secondary schools
Bibliographic record
Abstract
Longitudinal studies carried out by Statistics Canada and Human Resources Development Canada (Bowlby & McMullen, 2002) identified that dropout rates are high in Canada in relation to other developed nations. In 2003, Alberta's Commission on Learning found that one quarter of Alberta's high school enrollees were not completing high school. The Commission proposed the formation of learning communities as one way to increase the achievement of students in Alberta, with the intention that this would then increase the number of students completing high school. This research was undertaken to ascertain how mature the learning communities were in the high schools in Zone 6 of Southern Alberta, and whether there was a relationship between the maturity of a school's learning community and the school's achievement and high school completion rates. As the findings demonstrate, some relationships may have existed between the maturity of the schools' learning community and the diploma examination results, especially in Social Studies. Correlations were not found between the maturity of a school's learning community and eligibility for Rutherford Scholarships, the percentage of students taking four or more diploma examinations, and high school completion rates. The research did show, however, the levels of maturity in each of the learning communities at the time of this study, and the areas requiring further attention. The dimension of the learning communities requiring the most attention was found to be in the area of peer observation and feedback.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".