Issues and underlying factors relating to the graduation rates of Aboriginal students from mathematics programs
Bibliographic record
Abstract
The intent of my research was to shed light on the factors that impede Aboriginal students from finding success in their formal mathematical instruction within the Ontario provincial education system. The goal of my research was to provide a direction with which to proceed into the future. Specifically, my research was an attempt to explore the issues surrounding the high dropout rate of Aboriginal students. This research project is critically important because recent data is indicating that Aboriginals continue to struggle in school with dropout rates far above the national average (Auditor General of Canada, 2010; Banff International Research Station for Mathematical Innovation and Discovery, 2009). The aligning of my ontology, my understanding of epistemology and my methodology was critical in this process. My ontology encompasses a Euro-western perspective but has been swayed by the Aboriginal perspectives of my husband and family. My epistemology includes a mathematical framework combined with an attempt to understand situations through narrative. This aligning process has required incorporation of a unique ethno-epistemology, as I am also working with First Nations people. \nThe study involved First Nations participants who had completed their elementary education in their community. Participants who had completed a partial elementary program in their community, with the remainder in provincial schools were also included in the study. All students had transferred their elementary mathematics education to provincial Lakehead Public Schools Board secondary schools. None of the participants had completed post-secondary education. Therefore, the participants were asked questions pertaining to their early mathematical experiences through to their secondary mathematics education encounters, in an attempt to discover why they dropped out of their high school mathematics program.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".