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Record W1488640551

Learning communities, achievement and completion: Exploring relationships in southern Alberta secondary schools

2007· article· en· W1488640551 on OpenAlexaboutno aff
Corrienne Janet Beres

Bibliographic record

VenueThe Mathematics Enthusiast · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationAcademic achievementPedagogyPsychologyPolitical sciencePublic relations
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.150
GPT teacher head0.334
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

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