Ontario's Grade 6 Learners' Mathematics Achievement Profiles Underlying the EQAO Junior Division Assessment
Bibliographic record
Abstract
The popular approach of using overly simplistic total scores as the sole indicator of learner performances in the complex discipline of mathematics, restricts the recognition of remedial needs (Nichols, 1994). This study analyzed a random sample of 5,000 Grade 6 learner responses, out of 127,302 to the 2013-2014 EQAO assessment, to better understand mathematical performances. Firstly, an Exploratory Factor Analysis was performed to investigate the dimensionality of the test, followed by an investigation of strengths and weaknesses in these dimensions using a Latent Class Analysis (Collins Lanza, 2010). Results revealed that the majority of learners were strong in their ability to apply mathematical knowledge to solve problems, but weak in applying process or thinking skills to do the same. This pattern was consistent regardless of linguistic background. These results highlight the urgent need to not only remediate learnersâ mathematical thinking abilities, but also future research into this skill dilemma.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".