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Record W2614275400

Changes in School Results in EQAO Assessments from 2006 to 2010

2012· dissertation· en· W2614275400 on OpenAlexaboutno aff
Anita Ram

Bibliographic record

VenueTSpace (University of Toronto) · 2012
Typedissertation
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychology
DOInot available

Abstract

fetched live from OpenAlex

Many accountability systems use data from large-scale assessments to make judgements about school performance. In Ontario, school performance is often assessed using the percentage of proficient students (PPS). The purpose of this study was to shed light on the degree and frequency of changes from year to year in the percentage of proficient students, at a school, in the areas of reading, writing and mathematics for both grades 3 and 6 in Ontario from 2006 to 2010. A second purpose was to assess the influence of cohort size on the variability in scores from year to year. Once schools not having data for 5 consecutive years and outliers were omitted secondary data analysis was used to examine nearly 3000 schools in each subject and grade. For the first part of the study, descriptive statistics and frequencies were the main method of examination. In the second part of the study, variance scores and correlations were used in order to understand the relationship between changes in PPS and cohort size. Findings revealed that changes in school scores from year to year are very large for many schools. Approximately 50 percent of schools experienced changes in PPS greater than 10 percent in any given year. When examining how often, from 2006 to 2010, a school experienced a similar amount of change – generally, both the smallest and largest change categories had a larger percentage of schools experiencing a similar amount of change for two and three years. Very seldom did schools experience the same degree of change in PPS across all 5 years. Results from correlations revealed a significant and inverse relationship between average cohort size and variability in PPS. Considering over 80 percent of schools have 60 or fewer students in a cohort the unpredictability in PPS may prove to be quite frustrating to schools and confusing to stakeholders. Annual PPS scores appear to be a poor indicator of real school performance, and their use to rank or rate schools should be avoided. Recommendations are made about using PPS to report school level results for EQAO, schools and the public.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.073
GPT teacher head0.395
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2012
Admission routes1
Has abstractyes

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